From e1e727a2f39f14105f98e71c5de951f4267ea2b1 Mon Sep 17 00:00:00 2001 From: Vincentzyx <929403983@qq.com> Date: Tue, 7 Sep 2021 16:38:34 +0800 Subject: [PATCH] Init --- BidModel.py | 80 +++++ bid_weights.pkl | Bin 0 -> 4332929 bytes douzero/__init__.py | 0 douzero/dmc/__init__.py | 2 + douzero/dmc/arguments.py | 55 ++++ douzero/dmc/dmc.py | 252 +++++++++++++++ douzero/dmc/env_utils.py | 89 ++++++ douzero/dmc/file_writer.py | 188 +++++++++++ douzero/dmc/models.py | 480 +++++++++++++++++++++++++++++ douzero/dmc/utils.py | 199 ++++++++++++ douzero/evaluation/__init__.py | 0 douzero/evaluation/deep_agent.py | 62 ++++ douzero/evaluation/random_agent.py | 9 + douzero/evaluation/rlcard_agent.py | 183 +++++++++++ douzero/evaluation/simulation.py | 162 ++++++++++ douzero/radam/__init__.py | 1 + douzero/radam/radam.py | 244 +++++++++++++++ evaluate.py | 91 ++++++ generate_eval_data.py | 47 +++ imgs/douzero_logo.jpg | Bin 0 -> 39897 bytes requirements.txt | 4 + setup.py | 33 ++ train.py | 8 + 23 files changed, 2189 insertions(+) create mode 100644 BidModel.py create mode 100644 bid_weights.pkl create mode 100644 douzero/__init__.py create mode 100644 douzero/dmc/__init__.py create mode 100644 douzero/dmc/arguments.py create mode 100644 douzero/dmc/dmc.py create mode 100644 douzero/dmc/env_utils.py create mode 100644 douzero/dmc/file_writer.py create mode 100644 douzero/dmc/models.py create mode 100644 douzero/dmc/utils.py create mode 100644 douzero/evaluation/__init__.py create mode 100644 douzero/evaluation/deep_agent.py create mode 100644 douzero/evaluation/random_agent.py create mode 100644 douzero/evaluation/rlcard_agent.py create mode 100644 douzero/evaluation/simulation.py create mode 100644 douzero/radam/__init__.py create mode 100644 douzero/radam/radam.py create mode 100644 evaluate.py create mode 100644 generate_eval_data.py create mode 100644 imgs/douzero_logo.jpg create mode 100644 requirements.txt create mode 100644 setup.py create mode 100644 train.py diff --git a/BidModel.py b/BidModel.py new file mode 100644 index 0000000..c0108ea --- /dev/null +++ b/BidModel.py @@ -0,0 +1,80 @@ +# -*- coding: utf-8 -*- +# Created by: Vincentzyx +import os +import torch +from torch import nn +from torch.utils.data import DataLoader +from torch.utils.data.dataset import Dataset +import time + + +def EnvToOnehot(cards): + Env2IdxMap = {3:0,4:1,5:2,6:3,7:4,8:5,9:6,10:7,11:8,12:9,13:10,14:11,17:12,20:13,30:14} + cards = [Env2IdxMap[i] for i in cards] + Onehot = torch.zeros((4,15)) + for i in range(0, 15): + Onehot[:cards.count(i),i] = 1 + return Onehot + +def RealToOnehot(cards): + RealCard2EnvCard = {'3': 0, '4': 1, '5': 2, '6': 3, '7': 4, + '8': 5, '9': 6, 'T': 7, 'J': 8, 'Q': 9, + 'K': 10, 'A': 11, '2': 12, 'X': 13, 'D': 14} + cards = [RealCard2EnvCard[c] for c in cards] + Onehot = torch.zeros((4,15)) + for i in range(0, 15): + Onehot[:cards.count(i),i] = 1 + return Onehot + + +class Net(nn.Module): + def __init__(self): + super().__init__() + + self.fc1 = nn.Linear(60, 512) + self.fc2 = nn.Linear(512, 512) + self.fc3 = nn.Linear(512, 512) + self.fc4 = nn.Linear(512, 512) + self.fc5 = nn.Linear(512, 512) + self.fc6 = nn.Linear(512, 1) + self.dropout5 = nn.Dropout(0.5) + self.dropout3 = nn.Dropout(0.3) + self.dropout1 = nn.Dropout(0.1) + + def forward(self, input): + x = self.fc1(input) + x = torch.relu(self.dropout1(self.fc2(x))) + x = torch.relu(self.dropout3(self.fc3(x))) + x = torch.relu(self.dropout5(self.fc4(x))) + x = torch.relu(self.dropout5(self.fc5(x))) + x = self.fc6(x) + return x + + +UseGPU = False +device = torch.device('cuda:0') +net = Net() +net.eval() +if UseGPU: + net = net.to(device) +if os.path.exists("./bid_weights.pkl"): + if torch.cuda.is_available(): + net.load_state_dict(torch.load('./bid_weights.pkl')) + else: + net.load_state_dict(torch.load('./bid_weights.pkl', map_location=torch.device("cpu"))) + +def predict(cards): + input = RealToOnehot(cards) + if UseGPU: + input = input.to(device) + input = torch.flatten(input) + win_rate = net(input) + return win_rate[0].item() * 100 + +def predict_env(cards): + input = EnvToOnehot(cards) + if UseGPU: + input = input.to(device) + input = torch.flatten(input) + win_rate = net(input) + return win_rate[0].item() * 100 \ No newline at end of file diff --git a/bid_weights.pkl b/bid_weights.pkl new file mode 100644 index 0000000000000000000000000000000000000000..7ae9e3bc88c5ab55af4fcc4802ae8fc095d6b9b0 GIT binary patch literal 4332929 zcmZ^~2{@GP`^Rkysi>r+O$(((WC?SflS)(+DWz4Cl1j#sHf70@tQ9IPcCus%VXkv* z5m70!mo-t@mu%&o=l49n_jrEq|9Iy(9JtJUeeSvM`ra~6naW$AJ*T}zl;_a@Je>H<%r2ZeXKrS9=7N>2h`!B9a~t!MT4&7c zL|8m>hAiGy#v)wlQ)Y_t4(4Z0TiCJq%@7FGTaqso67RsUk#__vsv9ZTJirSZ@EsQh0>#{bBvvdF*X;2%cj zzZf_D!>IYU>}EUG7DLw7e;AejX4Lv0(T%Ka{}$E$52Mb18Mpt%xZ`iJopvl;Lzdn@ zj4J%hN7jsL@V z@V|_Q{$f1*x7ZOo)=@*&v40p<|I29d|H|=kuX;rV*71Mqnf^!b1XoX&m;0kT%j_S* zlYfhv+p$g=vQGa)sQNdd#s5kwvd;Wl(o&b#j&=4wif*iPe*vEVTg%Fhb-|EjZSTlp 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zGC99Hq}+*(3OqtQK7~R3&J_5mo=SEMs}qg8I~3k;;<&C*e%%9= zVeXp~=(6V(Sr=dlUVCiV^E?YSv8;@YSDpdaxTmXcKVQ7o{0PT#4UwEiSD51G4!?Xb zrq=BvH@O$F+jvXXIJ}taoa`5tUzY}n_Z%y!z6w?KU6{}$1CeqXEcJ^b>jcf@q75T%_Mi*C4A>|887JVg}$jjNXg^LFvCO+dJkL% zamJ6aLis4#u8QKiEs|_OA43-PX&^DMm13KUPU6Y;hV0Xwm2j)W0>`QLeXI3}DhceqN2t;rrnf^iq^#{UVN$ zeq}mD;mJeNO5|A2TZ^%~gONGzmE?Y>5~wWlhxTGA){*#}gs$90f)?g*4;UqO<;+_W z;;78hV~3I=MMIdcwie=?R|}47YRE9o^H?-Sg!7}rL5q8J$`0zo+GjbV^~-0%{su>; z_hYBvY~sXpB2D0wj*M_@Ss(TzY#wgga+UZPJs>MOUy-NoyGU!*O)~thDJzW&fZ}gY z$@orQ8=Q3&k8g-Lf8`I6bCxX+UA)M9=n#oX|F^Bb;;#(mPS(8fJ@^6q+5`M0 ze?9ywhZ)_YVT_TCgv77?SB~@h_U{>u{gMUYe@45oQ?n(WNJ{+LReuXz%)js7qa7Uo zjLixB(e=;s@_X#<7m~lnI{g_d^%fTWGgj@lcGchg8T&sPZrtq~$Fn$|ut`Y#9-Q>` z&*1;j^2Z~x2mhH3{(jV2-~I@8{`07(f6Q3>&!hf5HtCzj?|*UsKVwU35_e~tj%{~!C`Jv6{yR+ifo{MTkE zW%u8|N&KrT{Il_qQvZ0XhJVGcjmOFTdNeZl$8+9+_-it 0: + mean_episode_return_buf[position].append(torch.mean(episode_returns).to(device)) + with lock: + learner_outputs = model(obs_z, obs_x, return_value=True) + if position == "bidding": + pass + else: + loss = compute_loss(learner_outputs['values'], target) + stats = { + 'mean_episode_return_'+position: torch.mean(torch.stack([_r for _r in mean_episode_return_buf[position]])).item(), + 'loss_'+position: loss.item(), + } + + optimizer.zero_grad() + loss.backward() + nn.utils.clip_grad_norm_(model.parameters(), flags.max_grad_norm) + optimizer.step() + + for actor_model in actor_models.values(): + actor_model.get_model(position).load_state_dict(model.state_dict()) + return stats + +def train(flags): + """ + This is the main funtion for training. It will first + initilize everything, such as buffers, optimizers, etc. + Then it will start subprocesses as actors. Then, it will call + learning function with multiple threads. + """ + if not flags.actor_device_cpu or flags.training_device != 'cpu': + if not torch.cuda.is_available(): + raise AssertionError("CUDA not available. If you have GPUs, please specify the ID after `--gpu_devices`. Otherwise, please train with CPU with `python3 train.py --actor_device_cpu --training_device cpu`") + plogger = FileWriter( + xpid=flags.xpid, + xp_args=flags.__dict__, + rootdir=flags.savedir, + ) + checkpointpath = os.path.expandvars( + os.path.expanduser('%s/%s/%s' % (flags.savedir, flags.xpid, 'model.tar'))) + + T = flags.unroll_length + B = flags.batch_size + + if flags.actor_device_cpu: + device_iterator = ['cpu'] + else: + device_iterator = range(flags.num_actor_devices) + assert flags.num_actor_devices <= len(flags.gpu_devices.split(',')), 'The number of actor devices can not exceed the number of available devices' + + # Initialize actor models + models = {} + for device in device_iterator: + model = Model(device="cpu") + model.share_memory() + model.eval() + models[device] = model + + # Initialize queues + actor_processes = [] + ctx = mp.get_context('spawn') + batch_queues = {"landlord": ctx.SimpleQueue(), "landlord_up": ctx.SimpleQueue(), "landlord_down": ctx.SimpleQueue(), "bidding": ctx.SimpleQueue()} + + # Learner model for training + learner_model = Model(device=flags.training_device) + + # Create optimizers + optimizers = create_optimizers(flags, learner_model) + + # Stat Keys + stat_keys = [ + 'mean_episode_return_landlord', + 'loss_landlord', + 'mean_episode_return_landlord_up', + 'loss_landlord_up', + 'mean_episode_return_landlord_down', + 'loss_landlord_down', + 'mean_episode_return_bidding', + 'loss_bidding', + ] + frames, stats = 0, {k: 0 for k in stat_keys} + position_frames = {'landlord':0, 'landlord_up':0, 'landlord_down':0, 'bidding': 0} + + # Load models if any + if flags.load_model and os.path.exists(checkpointpath): + checkpoint_states = torch.load( + checkpointpath, map_location=("cuda:"+str(flags.training_device) if flags.training_device != "cpu" else "cpu") + ) + for k in ['landlord', 'landlord_up', 'landlord_down', 'bidding']: # ['landlord', 'landlord_up', 'landlord_down'] + learner_model.get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k]) + optimizers[k].load_state_dict(checkpoint_states["optimizer_state_dict"][k]) + for device in device_iterator: + models[device].get_model(k).load_state_dict(checkpoint_states["model_state_dict"][k]) + stats = checkpoint_states["stats"] + + if not 'mean_episode_return_bidding' in stats: + stats.update({"mean_episode_return_bidding": 0}) + if not 'loss_bidding' in stats: + stats.update({"loss_bidding": 0}) + frames = checkpoint_states["frames"] + position_frames = checkpoint_states["position_frames"] + if not "bidding" in position_frames: + position_frames.update({"bidding": 0}) + log.info(f"Resuming preempted job, current stats:\n{stats}") + + # Starting actor processes + for device in device_iterator: + num_actors = flags.num_actors + for i in range(flags.num_actors): + actor = mp.Process( + target=act, + args=(i, device, batch_queues, models[device], flags)) + # actor.setDaemon(True) + actor.start() + actor_processes.append(actor) + + def batch_and_learn(i, device, position, local_lock, position_lock, lock=threading.Lock()): + """Thread target for the learning process.""" + nonlocal frames, position_frames, stats + while frames < flags.total_frames: + batch = get_batch(batch_queues, position, flags, local_lock) + _stats = learn(position, models, learner_model.get_model(position), batch, + optimizers[position], flags, position_lock) + with lock: + for k in _stats: + stats[k] = _stats[k] + to_log = dict(frames=frames) + to_log.update({k: stats[k] for k in stat_keys}) + plogger.log(to_log) + frames += T * B + position_frames[position] += T * B + + + threads = [] + locks = {} + for device in device_iterator: + locks[device] = {'landlord': threading.Lock(), 'landlord_up': threading.Lock(), 'landlord_down': threading.Lock(), 'bidding': threading.Lock()} + position_locks = {'landlord': threading.Lock(), 'landlord_up': threading.Lock(), 'landlord_down': threading.Lock(), 'bidding': threading.Lock()} + + for device in device_iterator: + for i in range(flags.num_threads): + for position in ['landlord', 'landlord_up', 'landlord_down', 'bidding']: + thread = threading.Thread( + target=batch_and_learn, name='batch-and-learn-%d' % i, args=(i,device,position,locks[device][position],position_locks[position])) + thread.start() + threads.append(thread) + + def checkpoint(frames): + if flags.disable_checkpoint: + return + log.info('Saving checkpoint to %s', checkpointpath) + _models = learner_model.get_models() + torch.save({ + 'model_state_dict': {k: _models[k].state_dict() for k in _models}, # {{"general": _models["landlord"].state_dict()} + 'optimizer_state_dict': {k: optimizers[k].state_dict() for k in optimizers}, # {"general": optimizers["landlord"].state_dict()} + "stats": stats, + 'flags': vars(flags), + 'frames': frames, + 'position_frames': position_frames + }, checkpointpath) + + # Save the weights for evaluation purpose + for position in ['landlord', 'landlord_up', 'landlord_down', 'bidding']: # ['landlord', 'landlord_up', 'landlord_down'] + model_weights_dir = os.path.expandvars(os.path.expanduser( + '%s/%s/%s' % (flags.savedir, flags.xpid, "general_"+position+'_'+str(frames)+'.ckpt'))) + torch.save(learner_model.get_model(position).state_dict(), model_weights_dir) + + fps_log = [] + timer = timeit.default_timer + try: + last_checkpoint_time = timer() - flags.save_interval * 60 + while frames < flags.total_frames: + start_frames = frames + position_start_frames = {k: position_frames[k] for k in position_frames} + start_time = timer() + time.sleep(5) + + if timer() - last_checkpoint_time > flags.save_interval * 60: + checkpoint(frames) + last_checkpoint_time = timer() + end_time = timer() + + fps = (frames - start_frames) / (end_time - start_time) + fps_log.append(fps) + if len(fps_log) > 24: + fps_log = fps_log[1:] + fps_avg = np.mean(fps_log) + + position_fps = {k:(position_frames[k]-position_start_frames[k])/(end_time-start_time) for k in position_frames} + log.info('After %i (L:%i U:%i D:%i) frames: @ %.1f fps (avg@ %.1f fps) (L:%.1f U:%.1f D:%.1f) Stats:\n%s', + frames, + position_frames['landlord'], + position_frames['landlord_up'], + position_frames['landlord_down'], + fps, + fps_avg, + position_fps['landlord'], + position_fps['landlord_up'], + position_fps['landlord_down'], + pprint.pformat(stats)) + + except KeyboardInterrupt: + return + else: + for thread in threads: + thread.join() + log.info('Learning finished after %d frames.', frames) + + checkpoint(frames) + plogger.close() diff --git a/douzero/dmc/env_utils.py b/douzero/dmc/env_utils.py new file mode 100644 index 0000000..962fb88 --- /dev/null +++ b/douzero/dmc/env_utils.py @@ -0,0 +1,89 @@ +""" +Here, we wrap the original environment to make it easier +to use. When a game is finished, instead of mannualy reseting +the environment, we do it automatically. +""" +import numpy as np +import torch + +def _format_observation(obs, device): + """ + A utility function to process observations and + move them to CUDA. + """ + position = obs['position'] + if not device == "cpu": + device = 'cuda:' + str(device) + device = torch.device(device) + x_batch = torch.from_numpy(obs['x_batch']).to(device) + z_batch = torch.from_numpy(obs['z_batch']).to(device) + x_no_action = torch.from_numpy(obs['x_no_action']) + z = torch.from_numpy(obs['z']) + obs = {'x_batch': x_batch, + 'z_batch': z_batch, + 'legal_actions': obs['legal_actions'], + } + return position, obs, x_no_action, z + +class Environment: + def __init__(self, env, device): + """ Initialzie this environment wrapper + """ + self.env = env + self.device = device + self.episode_return = None + + def initial(self, model, device, flags=None): + obs, buf = self.env.reset(model, device, flags=flags) + initial_position, initial_obs, x_no_action, z = _format_observation(obs, self.device) + initial_reward = torch.zeros(1, 1) + self.episode_return = torch.zeros(1, 1) + initial_done = torch.ones(1, 1, dtype=torch.bool) + if buf is None: + return initial_position, initial_obs, dict( + done=initial_done, + episode_return=self.episode_return, + obs_x_no_action=x_no_action, + obs_z=z, + ) + else: + return initial_position, initial_obs, dict( + done=initial_done, + episode_return=self.episode_return, + obs_x_no_action=x_no_action, + obs_z=z, + begin_buf=buf + ) + + def step(self, action, model, device, flags=None): + obs, reward, done, _ = self.env.step(action) + + self.episode_return = reward + episode_return = self.episode_return + buf = None + if done: + obs, buf = self.env.reset(model, device, flags=flags) + self.episode_return = torch.zeros(1, 1) + + position, obs, x_no_action, z = _format_observation(obs, self.device) + # reward = torch.tensor(reward).view(1, 1) + done = torch.tensor(done).view(1, 1) + + if buf is None: + return position, obs, dict( + done=done, + episode_return=episode_return, + obs_x_no_action=x_no_action, + obs_z=z, + ) + else: + return position, obs, dict( + done=done, + episode_return=episode_return, + obs_x_no_action=x_no_action, + obs_z=z, + begin_buf=buf + ) + + def close(self): + self.env.close() diff --git a/douzero/dmc/file_writer.py b/douzero/dmc/file_writer.py new file mode 100644 index 0000000..d099009 --- /dev/null +++ b/douzero/dmc/file_writer.py @@ -0,0 +1,188 @@ +# Copyright (c) Facebook, Inc. and its affiliates. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import copy +import datetime +import csv +import json +import logging +import os +import time +from typing import Dict + +# import git + + +# def gather_metadata() -> Dict: +# date_start = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S.%f') +# # gathering git metadata +# try: +# repo = git.Repo(search_parent_directories=True) +# git_sha = repo.commit().hexsha +# git_data = dict( +# commit=git_sha, +# branch=repo.active_branch.name, +# is_dirty=repo.is_dirty(), +# path=repo.git_dir, +# ) +# except git.InvalidGitRepositoryError: +# git_data = None +# # gathering slurm metadata +# if 'SLURM_JOB_ID' in os.environ: +# slurm_env_keys = [k for k in os.environ if k.startswith('SLURM')] +# slurm_data = {} +# for k in slurm_env_keys: +# d_key = k.replace('SLURM_', '').replace('SLURMD_', '').lower() +# slurm_data[d_key] = os.environ[k] +# else: +# slurm_data = None +# return dict( +# date_start=date_start, +# date_end=None, +# successful=False, +# git=git_data, +# slurm=slurm_data, +# env=os.environ.copy(), +# ) + + +class FileWriter: + def __init__(self, + xpid: str = None, + xp_args: dict = None, + rootdir: str = '~/palaas'): + if not xpid: + # make unique id + xpid = '{proc}_{unixtime}'.format( + proc=os.getpid(), unixtime=int(time.time())) + self.xpid = xpid + self._tick = 0 + + # metadata gathering + if xp_args is None: + xp_args = {} + # self.metadata = gather_metadata() + # # we need to copy the args, otherwise when we close the file writer + # # (and rewrite the args) we might have non-serializable objects (or + # # other nasty stuff). + # self.metadata['args'] = copy.deepcopy(xp_args) + # self.metadata['xpid'] = self.xpid + + formatter = logging.Formatter('%(message)s') + self._logger = logging.getLogger('palaas/out') + + # to stdout handler + shandle = logging.StreamHandler() + shandle.setFormatter(formatter) + self._logger.addHandler(shandle) + self._logger.setLevel(logging.INFO) + + rootdir = os.path.expandvars(os.path.expanduser(rootdir)) + # to file handler + self.basepath = os.path.join(rootdir, self.xpid) + + if not os.path.exists(self.basepath): + self._logger.info('Creating log directory: %s', self.basepath) + os.makedirs(self.basepath, exist_ok=True) + else: + self._logger.info('Found log directory: %s', self.basepath) + + # NOTE: remove latest because it creates errors when running on slurm + # multiple jobs trying to write to latest but cannot find it + # Add 'latest' as symlink unless it exists and is no symlink. + # symlink = os.path.join(rootdir, 'latest') + # if os.path.islink(symlink): + # os.remove(symlink) + # if not os.path.exists(symlink): + # os.symlink(self.basepath, symlink) + # self._logger.info('Symlinked log directory: %s', symlink) + + self.paths = dict( + msg='{base}/out.log'.format(base=self.basepath), + logs='{base}/logs.csv'.format(base=self.basepath), + fields='{base}/fields.csv'.format(base=self.basepath), + meta='{base}/meta.json'.format(base=self.basepath), + ) + + self._logger.info('Saving arguments to %s', self.paths['meta']) + if os.path.exists(self.paths['meta']): + self._logger.warning('Path to meta file already exists. ' + 'Not overriding meta.') + else: + self._save_metadata() + + self._logger.info('Saving messages to %s', self.paths['msg']) + if os.path.exists(self.paths['msg']): + self._logger.warning('Path to message file already exists. ' + 'New data will be appended.') + + fhandle = logging.FileHandler(self.paths['msg']) + fhandle.setFormatter(formatter) + self._logger.addHandler(fhandle) + + self._logger.info('Saving logs data to %s', self.paths['logs']) + self._logger.info('Saving logs\' fields to %s', self.paths['fields']) + if os.path.exists(self.paths['logs']): + self._logger.warning('Path to log file already exists. ' + 'New data will be appended.') + with open(self.paths['fields'], 'r') as csvfile: + reader = csv.reader(csvfile) + self.fieldnames = list(reader)[0] + else: + self.fieldnames = ['_tick', '_time'] + + def log(self, to_log: Dict, tick: int = None, + verbose: bool = False) -> None: + if tick is not None: + raise NotImplementedError + else: + to_log['_tick'] = self._tick + self._tick += 1 + to_log['_time'] = time.time() + + old_len = len(self.fieldnames) + for k in to_log: + if k not in self.fieldnames: + self.fieldnames.append(k) + if old_len != len(self.fieldnames): + with open(self.paths['fields'], 'w') as csvfile: + writer = csv.writer(csvfile) + writer.writerow(self.fieldnames) + self._logger.info('Updated log fields: %s', self.fieldnames) + + if to_log['_tick'] == 0: + # print("\ncreating logs file ") + with open(self.paths['logs'], 'a') as f: + f.write('# %s\n' % ','.join(self.fieldnames)) + + if verbose: + self._logger.info('LOG | %s', ', '.join( + ['{}: {}'.format(k, to_log[k]) for k in sorted(to_log)])) + + with open(self.paths['logs'], 'a') as f: + writer = csv.DictWriter(f, fieldnames=self.fieldnames) + writer.writerow(to_log) + # print("\nadded to log file") + + def close(self, successful: bool = True) -> None: + # self.metadata['date_end'] = datetime.datetime.now().strftime( + # '%Y-%m-%d %H:%M:%S.%f') + # self.metadata['successful'] = successful + self._save_metadata() + + def _save_metadata(self) -> None: + with open(self.paths['meta'], 'w') as jsonfile: + pass + # json.dump(self.metadata, jsonfile, indent=4, sort_keys=True) diff --git a/douzero/dmc/models.py b/douzero/dmc/models.py new file mode 100644 index 0000000..c333115 --- /dev/null +++ b/douzero/dmc/models.py @@ -0,0 +1,480 @@ +""" +This file includes the torch models. We wrap the three +models into one class for convenience. +""" + +import numpy as np + +import torch +from torch import nn +import torch.nn.functional as F + +class LandlordLstmModel(nn.Module): + def __init__(self): + super().__init__() + self.lstm = nn.LSTM(162, 128, batch_first=True) + self.dense1 = nn.Linear(373 + 128, 512) + self.dense2 = nn.Linear(512, 512) + self.dense3 = nn.Linear(512, 512) + self.dense4 = nn.Linear(512, 512) + self.dense5 = nn.Linear(512, 512) + self.dense6 = nn.Linear(512, 1) + + def forward(self, z, x, return_value=False, flags=None): + lstm_out, (h_n, _) = self.lstm(z) + lstm_out = lstm_out[:,-1,:] + x = torch.cat([lstm_out,x], dim=-1) + x = self.dense1(x) + x = torch.relu(x) + x = self.dense2(x) + x = torch.relu(x) + x = self.dense3(x) + x = torch.relu(x) + x = self.dense4(x) + x = torch.relu(x) + x = self.dense5(x) + x = torch.relu(x) + x = self.dense6(x) + if return_value: + return dict(values=x) + else: + if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon: + action = torch.randint(x.shape[0], (1,))[0] + else: + action = torch.argmax(x,dim=0)[0] + return dict(action=action) + +class FarmerLstmModel(nn.Module): + def __init__(self): + super().__init__() + self.lstm = nn.LSTM(162, 128, batch_first=True) + self.dense1 = nn.Linear(484 + 128, 512) + self.dense2 = nn.Linear(512, 512) + self.dense3 = nn.Linear(512, 512) + self.dense4 = nn.Linear(512, 512) + self.dense5 = nn.Linear(512, 512) + self.dense6 = nn.Linear(512, 1) + + def forward(self, z, x, return_value=False, flags=None): + lstm_out, (h_n, _) = self.lstm(z) + lstm_out = lstm_out[:,-1,:] + x = torch.cat([lstm_out,x], dim=-1) + x = self.dense1(x) + x = torch.relu(x) + x = self.dense2(x) + x = torch.relu(x) + x = self.dense3(x) + x = torch.relu(x) + x = self.dense4(x) + x = torch.relu(x) + x = self.dense5(x) + x = torch.relu(x) + x = self.dense6(x) + if return_value: + return dict(values=x) + else: + if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon: + action = torch.randint(x.shape[0], (1,))[0] + else: + action = torch.argmax(x,dim=0)[0] + return dict(action=action) + +class LandlordLstmNewModel(nn.Module): + def __init__(self): + super().__init__() + self.lstm = nn.LSTM(162, 128, batch_first=True) + self.dense1 = nn.Linear(373 + 128, 512) + self.dense2 = nn.Linear(512, 512) + self.dense3 = nn.Linear(512, 512) + self.dense4 = nn.Linear(512, 512) + self.dense5 = nn.Linear(512, 512) + self.dense6 = nn.Linear(512, 1) + + def forward(self, z, x, return_value=False, flags=None): + lstm_out, (h_n, _) = self.lstm(z) + lstm_out = lstm_out[:,-1,:] + x = torch.cat([lstm_out,x], dim=-1) + x = self.dense1(x) + x = torch.relu(x) + x = self.dense2(x) + x = torch.relu(x) + x = self.dense3(x) + x = torch.relu(x) + x = self.dense4(x) + x = torch.relu(x) + x = self.dense5(x) + x = torch.relu(x) + x = self.dense6(x) + if return_value: + return dict(values=x) + else: + if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon: + action = torch.randint(x.shape[0], (1,))[0] + else: + action = torch.argmax(x,dim=0)[0] + return dict(action=action) + +class FarmerLstmNewModel(nn.Module): + def __init__(self): + super().__init__() + self.lstm = nn.LSTM(162, 128, batch_first=True) + self.dense1 = nn.Linear(484 + 128, 512) + self.dense2 = nn.Linear(512, 512) + self.dense3 = nn.Linear(512, 512) + self.dense4 = nn.Linear(512, 512) + self.dense5 = nn.Linear(512, 512) + self.dense6 = nn.Linear(512, 1) + + def forward(self, z, x, return_value=False, flags=None): + lstm_out, (h_n, _) = self.lstm(z) + lstm_out = lstm_out[:,-1,:] + x = torch.cat([lstm_out,x], dim=-1) + x = self.dense1(x) + x = torch.relu(x) + x = self.dense2(x) + x = torch.relu(x) + x = self.dense3(x) + x = torch.relu(x) + x = self.dense4(x) + x = torch.relu(x) + x = self.dense5(x) + x = torch.relu(x) + x = self.dense6(x) + if return_value: + return dict(values=x) + else: + if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon: + action = torch.randint(x.shape[0], (1,))[0] + else: + action = torch.argmax(x,dim=0)[0] + return dict(action=action) + +class GeneralModel1(nn.Module): + def __init__(self): + super().__init__() + # input: B * 32 * 57 + # self.lstm = nn.LSTM(162, 512, batch_first=True) + self.conv_z_1 = torch.nn.Sequential( + nn.Conv2d(1, 64, kernel_size=(1,57)), # B * 1 * 64 * 32 + nn.ReLU(inplace=True), + nn.BatchNorm2d(64), + ) + # Squeeze(-1) B * 64 * 16 + self.conv_z_2 = torch.nn.Sequential( + nn.Conv1d(64, 128, kernel_size=(5,), padding=2), # 128 * 16 + nn.ReLU(inplace=True), + nn.BatchNorm1d(128), + ) + self.conv_z_3 = torch.nn.Sequential( + nn.Conv1d(128, 256, kernel_size=(3,), padding=1), # 256 * 8 + nn.ReLU(inplace=True), + nn.BatchNorm1d(256), + + ) + self.conv_z_4 = torch.nn.Sequential( + nn.Conv1d(256, 512, kernel_size=(3,), padding=1), # 512 * 4 + nn.ReLU(inplace=True), + nn.BatchNorm1d(512), + + ) + + self.dense1 = nn.Linear(519 + 1024, 1024) + self.dense2 = nn.Linear(1024, 512) + self.dense3 = nn.Linear(512, 512) + self.dense4 = nn.Linear(512, 512) + self.dense5 = nn.Linear(512, 512) + self.dense6 = nn.Linear(512, 1) + + def forward(self, z, x, return_value=False, flags=None, debug=False): + z = z.unsqueeze(1) + z = self.conv_z_1(z) + z = z.squeeze(-1) + z = torch.max_pool1d(z, 2) + z = self.conv_z_2(z) + z = torch.max_pool1d(z, 2) + z = self.conv_z_3(z) + z = torch.max_pool1d(z, 2) + z = self.conv_z_4(z) + z = torch.max_pool1d(z, 2) + z = z.flatten(1,2) + x = torch.cat([z,x], dim=-1) + x = self.dense1(x) + x = torch.relu(x) + x = self.dense2(x) + x = torch.relu(x) + x = self.dense3(x) + x = torch.relu(x) + x = self.dense4(x) + x = torch.relu(x) + x = self.dense5(x) + x = torch.relu(x) + x = self.dense6(x) + if return_value: + return dict(values=x) + else: + if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon: + action = torch.randint(x.shape[0], (1,))[0] + else: + action = torch.argmax(x,dim=0)[0] + return dict(action=action, max_value=torch.max(x)) + + +# 用于ResNet18和34的残差块,用的是2个3x3的卷积 +class BasicBlock(nn.Module): + expansion = 1 + + def __init__(self, in_planes, planes, stride=1): + super(BasicBlock, self).__init__() + self.conv1 = nn.Conv1d(in_planes, planes, kernel_size=(3,), + stride=(stride,), padding=1, bias=False) + self.bn1 = nn.BatchNorm1d(planes) + self.conv2 = nn.Conv1d(planes, planes, kernel_size=(3,), + stride=(1,), padding=1, bias=False) + self.bn2 = nn.BatchNorm1d(planes) + self.shortcut = nn.Sequential() + # 经过处理后的x要与x的维度相同(尺寸和深度) + # 如果不相同,需要添加卷积+BN来变换为同一维度 + if stride != 1 or in_planes != self.expansion * planes: + self.shortcut = nn.Sequential( + nn.Conv1d(in_planes, self.expansion * planes, + kernel_size=(1,), stride=(stride,), bias=False), + nn.BatchNorm1d(self.expansion * planes) + ) + + def forward(self, x): + out = F.relu(self.bn1(self.conv1(x))) + out = self.bn2(self.conv2(out)) + out += self.shortcut(x) + out = F.relu(out) + return out + + +class GeneralModel(nn.Module): + def __init__(self): + super().__init__() + self.in_planes = 80 + #input 1*54*41 + self.conv1 = nn.Conv1d(40, 80, kernel_size=(3,), + stride=(2,), padding=1, bias=False) #1*27*80 + + self.bn1 = nn.BatchNorm1d(80) + + self.layer1 = self._make_layer(BasicBlock, 80, 2, stride=2)#1*14*80 + self.layer2 = self._make_layer(BasicBlock, 160, 2, stride=2)#1*7*160 + self.layer3 = self._make_layer(BasicBlock, 320, 2, stride=2)#1*4*320 + # self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2) + self.linear1 = nn.Linear(320 * BasicBlock.expansion * 4 + 15 * 4, 1024) + self.linear2 = nn.Linear(1024, 512) + self.linear3 = nn.Linear(512, 256) + self.linear4 = nn.Linear(256, 1) + + def _make_layer(self, block, planes, num_blocks, stride): + strides = [stride] + [1] * (num_blocks - 1) + layers = [] + for stride in strides: + layers.append(block(self.in_planes, planes, stride)) + self.in_planes = planes * block.expansion + return nn.Sequential(*layers) + + def forward(self, z, x, return_value=False, flags=None, debug=False): + out = F.relu(self.bn1(self.conv1(z))) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = out.flatten(1,2) + out = torch.cat([x,x,x,x,out], dim=-1) + out = F.leaky_relu_(self.linear1(out)) + out = F.leaky_relu_(self.linear2(out)) + out = F.leaky_relu_(self.linear3(out)) + out = F.leaky_relu_(self.linear4(out)) + if return_value: + return dict(values=out) + else: + if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon: + action = torch.randint(out.shape[0], (1,))[0] + else: + action = torch.argmax(out,dim=0)[0] + return dict(action=action, max_value=torch.max(out)) + + + + + +class BidModel(nn.Module): + def __init__(self): + super().__init__() + + self.dense1 = nn.Linear(114, 512) + self.dense2 = nn.Linear(512, 512) + self.dense3 = nn.Linear(512, 512) + self.dense4 = nn.Linear(512, 512) + self.dense5 = nn.Linear(512, 512) + self.dense6 = nn.Linear(512, 1) + + def forward(self, z, x, return_value=False, flags=None, debug=False): + x = self.dense1(x) + x = F.leaky_relu(x) + # x = F.relu(x) + x = self.dense2(x) + x = F.leaky_relu(x) + # x = F.relu(x) + x = self.dense3(x) + x = F.leaky_relu(x) + # x = F.relu(x) + x = self.dense4(x) + x = F.leaky_relu(x) + # x = F.relu(x) + x = self.dense5(x) + # x = F.relu(x) + x = F.leaky_relu(x) + x = self.dense6(x) + if return_value: + return dict(values=x) + else: + if flags is not None and flags.exp_epsilon > 0 and np.random.rand() < flags.exp_epsilon: + action = torch.randint(x.shape[0], (1,))[0] + else: + action = torch.argmax(x,dim=0)[0] + return dict(action=action, max_value=torch.max(x)) + + +# Model dict is only used in evaluation but not training +model_dict = {} +model_dict['landlord'] = LandlordLstmModel +model_dict['landlord_up'] = FarmerLstmModel +model_dict['landlord_down'] = FarmerLstmModel +model_dict_new = {} +model_dict_new['landlord'] = GeneralModel +model_dict_new['landlord_up'] = GeneralModel +model_dict_new['landlord_down'] = GeneralModel +model_dict_new['bidding'] = BidModel +model_dict_lstm = {} +model_dict_lstm['landlord'] = GeneralModel +model_dict_lstm['landlord_up'] = GeneralModel +model_dict_lstm['landlord_down'] = GeneralModel + +class General_Model: + """ + The wrapper for the three models. We also wrap several + interfaces such as share_memory, eval, etc. + """ + def __init__(self, device=0): + self.models = {} + if not device == "cpu": + device = 'cuda:' + str(device) + # model = GeneralModel().to(torch.device(device)) + self.models['landlord'] = GeneralModel1().to(torch.device(device)) + self.models['landlord_up'] = GeneralModel1().to(torch.device(device)) + self.models['landlord_down'] = GeneralModel1().to(torch.device(device)) + self.models['bidding'] = BidModel().to(torch.device(device)) + + def forward(self, position, z, x, training=False, flags=None, debug=False): + model = self.models[position] + return model.forward(z, x, training, flags, debug) + + def share_memory(self): + self.models['landlord'].share_memory() + self.models['landlord_up'].share_memory() + self.models['landlord_down'].share_memory() + self.models['bidding'].share_memory() + + def eval(self): + self.models['landlord'].eval() + self.models['landlord_up'].eval() + self.models['landlord_down'].eval() + self.models['bidding'].eval() + + def parameters(self, position): + return self.models[position].parameters() + + def get_model(self, position): + return self.models[position] + + def get_models(self): + return self.models + +class OldModel: + """ + The wrapper for the three models. We also wrap several + interfaces such as share_memory, eval, etc. + """ + def __init__(self, device=0): + self.models = {} + if not device == "cpu": + device = 'cuda:' + str(device) + self.models['landlord'] = LandlordLstmModel().to(torch.device(device)) + self.models['landlord_up'] = FarmerLstmModel().to(torch.device(device)) + self.models['landlord_down'] = FarmerLstmModel().to(torch.device(device)) + + def forward(self, position, z, x, training=False, flags=None): + model = self.models[position] + return model.forward(z, x, training, flags) + + def share_memory(self): + self.models['landlord'].share_memory() + self.models['landlord_up'].share_memory() + self.models['landlord_down'].share_memory() + + def eval(self): + self.models['landlord'].eval() + self.models['landlord_up'].eval() + self.models['landlord_down'].eval() + + def parameters(self, position): + return self.models[position].parameters() + + def get_model(self, position): + return self.models[position] + + def get_models(self): + return self.models + + +class Model: + """ + The wrapper for the three models. We also wrap several + interfaces such as share_memory, eval, etc. + """ + def __init__(self, device=0): + self.models = {} + if not device == "cpu": + device = 'cuda:' + str(device) + # model = GeneralModel().to(torch.device(device)) + self.models['landlord'] = GeneralModel().to(torch.device(device)) + self.models['landlord_up'] = GeneralModel().to(torch.device(device)) + self.models['landlord_down'] = GeneralModel().to(torch.device(device)) + self.models['bidding'] = BidModel().to(torch.device(device)) + + def forward(self, position, z, x, training=False, flags=None, debug=False): + model = self.models[position] + return model.forward(z, x, training, flags, debug) + + def share_memory(self): + self.models['landlord'].share_memory() + self.models['landlord_up'].share_memory() + self.models['landlord_down'].share_memory() + self.models['bidding'].share_memory() + + def eval(self): + self.models['landlord'].eval() + self.models['landlord_up'].eval() + self.models['landlord_down'].eval() + self.models['bidding'].eval() + + def parameters(self, position): + return self.models[position].parameters() + + def get_model(self, position): + return self.models[position] + + def get_models(self): + return self.models + + + + + + + + + diff --git a/douzero/dmc/utils.py b/douzero/dmc/utils.py new file mode 100644 index 0000000..1ed0c7d --- /dev/null +++ b/douzero/dmc/utils.py @@ -0,0 +1,199 @@ +import os +import typing +import logging +import traceback +import numpy as np +from collections import Counter +import time +from douzero.radam.radam import RAdam + +import torch +from torch import multiprocessing as mp + +from .env_utils import Environment +from douzero.env import Env + +Card2Column = {3: 0, 4: 1, 5: 2, 6: 3, 7: 4, 8: 5, 9: 6, 10: 7, + 11: 8, 12: 9, 13: 10, 14: 11, 17: 12} + +NumOnes2Array = {0: np.array([0, 0, 0, 0]), + 1: np.array([1, 0, 0, 0]), + 2: np.array([1, 1, 0, 0]), + 3: np.array([1, 1, 1, 0]), + 4: np.array([1, 1, 1, 1])} + +shandle = logging.StreamHandler() +shandle.setFormatter( + logging.Formatter( + '[%(levelname)s:%(process)d %(module)s:%(lineno)d %(asctime)s] ' + '%(message)s')) +log = logging.getLogger('doudzero') +log.propagate = False +log.addHandler(shandle) +log.setLevel(logging.INFO) + +# Buffers are used to transfer data between actor processes +# and learner processes. They are shared tensors in GPU +Buffers = typing.Dict[str, typing.List[torch.Tensor]] + +def create_env(flags): + return Env(flags.objective) + +def get_batch(b_queues, position, flags, lock): + """ + This function will sample a batch from the buffers based + on the indices received from the full queue. It will also + free the indices by sending it to full_queue. + """ + b_queue = b_queues[position] + buffer = [] + while len(buffer) < flags.batch_size: + buffer.append(b_queue.get()) + batch = { + key: torch.stack([m[key] for m in buffer], dim=1) + for key in ["done", "episode_return", "target", "obs_z", "obs_x_batch", "obs_type"] + } + del buffer + return batch + +def create_optimizers(flags, learner_model): + """ + Create three optimizers for the three positions + """ + positions = ['landlord', 'landlord_up', 'landlord_down', 'bidding'] + optimizers = {} + for position in positions: + optimizer = RAdam( + learner_model.parameters(position), + lr=flags.learning_rate, + eps=flags.epsilon) + optimizers[position] = optimizer + return optimizers + + +def act(i, device, batch_queues, model, flags): + positions = ['landlord', 'landlord_up', 'landlord_down', 'bidding'] + for pos in positions: + model.models[pos].to(torch.device(device if device == "cpu" else ("cuda:"+str(device)))) + try: + T = flags.unroll_length + log.info('Device %s Actor %i started.', str(device), i) + + env = create_env(flags) + env = Environment(env, device) + + done_buf = {p: [] for p in positions} + episode_return_buf = {p: [] for p in positions} + target_buf = {p: [] for p in positions} + obs_z_buf = {p: [] for p in positions} + size = {p: 0 for p in positions} + type_buf = {p: [] for p in positions} + obs_x_batch_buf = {p: [] for p in positions} + + position_index = {"landlord": 31, "landlord_up": 32, "landlord_down": 33} + bid_type_index = {"landlord": 41, "landlord_up": 42, "landlord_down": 43} + bid_type_map = {41: "landlord", 42: "landlord_up", 43: "landlord_down"} + + position, obs, env_output = env.initial(model, device, flags=flags) + bid_obs_buffer = env_output["begin_buf"]["bid_obs_buffer"] + multiply_obs_buffer = env_output["begin_buf"]["multiply_obs_buffer"] + while True: + # print("posi", position) + for bid_obs in bid_obs_buffer: + obs_z_buf["bidding"].append(bid_obs['z_batch']) + obs_x_batch_buf["bidding"].append(bid_obs["x_batch"]) + type_buf["bidding"].append(bid_type_index[bid_obs["position"]]) + size["bidding"] += 1 + for mul_obs in multiply_obs_buffer: + obs_z_buf[mul_obs["position"]].append(mul_obs['z_batch']) + obs_x_batch_buf[mul_obs["position"]].append(mul_obs["x_batch"]) + type_buf[mul_obs["position"]].append(2) + size[mul_obs["position"]] += 1 + while True: + + with torch.no_grad(): + agent_output = model.forward(position, obs['z_batch'], obs['x_batch'], flags=flags) + _action_idx = int(agent_output['action'].cpu().detach().numpy()) + action = obs['legal_actions'][_action_idx] + obs_z_buf[position].append(torch.vstack((_cards2tensor(action).unsqueeze(0), env_output['obs_z'])).float()) + # x_batch = torch.cat((env_output['obs_x_no_action'], _cards2tensor(action)), dim=0).float() + x_batch = env_output['obs_x_no_action'].float() + obs_x_batch_buf[position].append(x_batch) + type_buf[position].append(position_index[position]) + position, obs, env_output = env.step(action, model, device, flags=flags) + size[position] += 1 + if env_output['done']: + bid_obs_buffer = env_output["begin_buf"]["bid_obs_buffer"] + multiply_obs_buffer = env_output["begin_buf"]["multiply_obs_buffer"] + for p in positions: + diff = size[p] - len(target_buf[p]) + # print(p, diff) + if diff > 0: + done_buf[p].extend([False for _ in range(diff-1)]) + done_buf[p].append(True) + if p != "bidding": + episode_return = env_output['episode_return']["play"][p] if p == 'landlord' else -env_output['episode_return']["play"][p] + episode_return_buf[p].extend([0.0 for _ in range(diff-1)]) + episode_return_buf[p].append(episode_return) + target_buf[p].extend([episode_return for _ in range(diff)]) + else: + offset = len(target_buf[p]) + for index in range(diff): + pos = type_buf[p][index+offset] + if pos == 41: + episode_return = env_output['episode_return']["bid"]["landlord"] + else: + episode_return = -env_output['episode_return']["bid"][bid_type_map[pos]] + episode_return_buf[p].append(episode_return) + # print(p, episode_return) + target_buf[p].append(episode_return) + break + for p in positions: + if size[p] > T: + # print(p, "epr", torch.stack([torch.tensor(ndarr, device="cpu") for ndarr in episode_return_buf[p][:T]]),) + batch_queues[p].put({ + "done": torch.stack([torch.tensor(ndarr, device="cpu") for ndarr in done_buf[p][:T]]), + "episode_return": torch.stack([torch.tensor(ndarr, device="cpu") for ndarr in episode_return_buf[p][:T]]), + "target": torch.stack([torch.tensor(ndarr, device="cpu") for ndarr in target_buf[p][:T]]), + "obs_z": torch.stack([torch.tensor(ndarr, device="cpu") for ndarr in obs_z_buf[p][:T]]), + "obs_x_batch": torch.stack([torch.tensor(ndarr, device="cpu") for ndarr in obs_x_batch_buf[p][:T]]), + "obs_type": torch.stack([torch.tensor(ndarr, device="cpu") for ndarr in type_buf[p][:T]]) + }) + done_buf[p] = done_buf[p][T:] + episode_return_buf[p] = episode_return_buf[p][T:] + target_buf[p] = target_buf[p][T:] + obs_x_batch_buf[p] = obs_x_batch_buf[p][T:] + obs_z_buf[p] = obs_z_buf[p][T:] + type_buf[p] = type_buf[p][T:] + size[p] -= T + + except KeyboardInterrupt: + pass + except Exception as e: + log.error('Exception in worker process %i', i) + traceback.print_exc() + print() + raise e + +def _cards2tensor(list_cards): + """ + Convert a list of integers to the tensor + representation + See Figure 2 in https://arxiv.org/pdf/2106.06135.pdf + """ + if len(list_cards) == 0: + return torch.zeros(54, dtype=torch.int8) + + matrix = np.zeros([4, 13], dtype=np.int8) + jokers = np.zeros(2, dtype=np.int8) + counter = Counter(list_cards) + for card, num_times in counter.items(): + if card < 20: + matrix[:, Card2Column[card]] = NumOnes2Array[num_times] + elif card == 20: + jokers[0] = 1 + elif card == 30: + jokers[1] = 1 + matrix = np.concatenate((matrix.flatten('F'), jokers)) + matrix = torch.from_numpy(matrix) + return matrix diff --git a/douzero/evaluation/__init__.py b/douzero/evaluation/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/douzero/evaluation/deep_agent.py b/douzero/evaluation/deep_agent.py new file mode 100644 index 0000000..43b78c8 --- /dev/null +++ b/douzero/evaluation/deep_agent.py @@ -0,0 +1,62 @@ +import torch +import numpy as np + +from douzero.env.env import get_obs + +def _load_model(position, model_path, model_type): + from douzero.dmc.models import model_dict_new, model_dict + model = None + if model_type == "general": + model = model_dict_new[position]() + else: + model = model_dict[position]() + model_state_dict = model.state_dict() + if torch.cuda.is_available(): + pretrained = torch.load(model_path, map_location='cuda:0') + else: + pretrained = torch.load(model_path, map_location='cpu') + pretrained = {k: v for k, v in pretrained.items() if k in model_state_dict} + model_state_dict.update(pretrained) + model.load_state_dict(model_state_dict) + # torch.save(model.state_dict(), model_path.replace(".ckpt", "_nobn.ckpt")) + if torch.cuda.is_available(): + model.cuda() + model.eval() + return model + +class DeepAgent: + + def __init__(self, position, model_path): + self.model_type = "general" if "resnet" in model_path else "old" + self.model = _load_model(position, model_path, self.model_type) + self.EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7', + 8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q', + 13: 'K', 14: 'A', 17: '2', 20: 'X', 30: 'D'} + def act(self, infoset): + if len(infoset.legal_actions) == 1: + return infoset.legal_actions[0] + + obs = get_obs(infoset, self.model_type == "general") + + z_batch = torch.from_numpy(obs['z_batch']).float() + x_batch = torch.from_numpy(obs['x_batch']).float() + if torch.cuda.is_available(): + z_batch, x_batch = z_batch.cuda(), x_batch.cuda() + y_pred = self.model.forward(z_batch, x_batch, return_value=True)['values'] + y_pred = y_pred.detach().cpu().numpy() + + best_action_index = np.argmax(y_pred, axis=0)[0] + best_action = infoset.legal_actions[best_action_index] + # action_list = [] + # output = "" + # for i, action in enumerate(y_pred): + # action_list.append((y_pred[i].item(), "".join([self.EnvCard2RealCard[ii] for ii in infoset.legal_actions[i]]) if len(infoset.legal_actions[i]) != 0 else "Pass")) + # action_list.sort(key=lambda x: x[0], reverse=True) + # value_list = [] + # for action in action_list: + # output += str(round(action[0],3)) + " " + action[1] + "\n" + # value_list.append(action[0]) + # # print(value_list) + # print(output) + # print("--------------------\n") + return best_action diff --git a/douzero/evaluation/random_agent.py b/douzero/evaluation/random_agent.py new file mode 100644 index 0000000..435ce1a --- /dev/null +++ b/douzero/evaluation/random_agent.py @@ -0,0 +1,9 @@ +import random + +class RandomAgent(): + + def __init__(self): + self.name = 'Random' + + def act(self, infoset): + return random.choice(infoset.legal_actions) diff --git a/douzero/evaluation/rlcard_agent.py b/douzero/evaluation/rlcard_agent.py new file mode 100644 index 0000000..b4c9643 --- /dev/null +++ b/douzero/evaluation/rlcard_agent.py @@ -0,0 +1,183 @@ +import random + +from rlcard.games.doudizhu.utils import CARD_TYPE + +EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7', + 8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q', + 13: 'K', 14: 'A', 17: '2', 20: 'B', 30: 'R'} +RealCard2EnvCard = {'3': 3, '4': 4, '5': 5, '6': 6, '7': 7, + '8': 8, '9': 9, 'T': 10, 'J': 11, 'Q': 12, + 'K': 13, 'A': 14, '2': 17, 'B': 20, 'R': 30} + +INDEX = {'3': 0, '4': 1, '5': 2, '6': 3, '7': 4, + '8': 5, '9': 6, 'T': 7, 'J': 8, 'Q': 9, + 'K': 10, 'A': 11, '2': 12, 'B': 13, 'R': 14} + +class RLCardAgent(object): + + def __init__(self, position): + self.name = 'RLCard' + self.position = position + + def act(self, infoset): + try: + # Hand cards + hand_cards = infoset.player_hand_cards + for i, c in enumerate(hand_cards): + hand_cards[i] = EnvCard2RealCard[c] + hand_cards = ''.join(hand_cards) + + # Last move + last_move = infoset.last_move.copy() + for i, c in enumerate(last_move): + last_move[i] = EnvCard2RealCard[c] + last_move = ''.join(last_move) + + # Last two moves + last_two_cards = infoset.last_two_moves + for i in range(2): + for j, c in enumerate(last_two_cards[i]): + last_two_cards[i][j] = EnvCard2RealCard[c] + last_two_cards[i] = ''.join(last_two_cards[i]) + + # Last pid + last_pid = infoset.last_pid + + action = None + # the rule of leading round + if last_two_cards[0] == '' and last_two_cards[1] == '': + chosen_action = None + comb = combine_cards(hand_cards) + min_card = hand_cards[0] + for _, acs in comb.items(): + for ac in acs: + if min_card in ac: + chosen_action = ac + action = [char for char in chosen_action] + for i, c in enumerate(action): + action[i] = RealCard2EnvCard[c] + #print('lead action:', action) + # the rule of following cards + else: + the_type = CARD_TYPE[0][last_move][0][0] + chosen_action = '' + rank = 1000 + for ac in infoset.legal_actions: + _ac = ac.copy() + for i, c in enumerate(_ac): + _ac[i] = EnvCard2RealCard[c] + _ac = ''.join(_ac) + if _ac != '' and the_type == CARD_TYPE[0][_ac][0][0]: + if int(CARD_TYPE[0][_ac][0][1]) < rank: + rank = int(CARD_TYPE[0][_ac][0][1]) + chosen_action = _ac + if chosen_action != '': + action = [char for char in chosen_action] + for i, c in enumerate(action): + action[i] = RealCard2EnvCard[c] + #print('action:', action) + elif last_pid != 'landlord' and self.position != 'landlord': + action = [] + + if action is None: + action = random.choice(infoset.legal_actions) + except: + action = random.choice(infoset.legal_actions) + #import traceback + #traceback.print_exc() + + assert action in infoset.legal_actions + + return action + +def card_str2list(hand): + hand_list = [0 for _ in range(15)] + for card in hand: + hand_list[INDEX[card]] += 1 + return hand_list + +def list2card_str(hand_list): + card_str = '' + cards = [card for card in INDEX] + for index, count in enumerate(hand_list): + card_str += cards[index] * count + return card_str + +def pick_chain(hand_list, count): + chains = [] + str_card = [card for card in INDEX] + hand_list = [str(card) for card in hand_list] + hand = ''.join(hand_list[:12]) + chain_list = hand.split('0') + add = 0 + for index, chain in enumerate(chain_list): + if len(chain) > 0: + if len(chain) >= 5: + start = index + add + min_count = int(min(chain)) // count + if min_count != 0: + str_chain = '' + for num in range(len(chain)): + str_chain += str_card[start+num] + hand_list[start+num] = int(hand_list[start+num]) - int(min(chain)) + for _ in range(min_count): + chains.append(str_chain) + add += len(chain) + hand_list = [int(card) for card in hand_list] + return (chains, hand_list) + +def combine_cards(hand): + '''Get optimal combinations of cards in hand + ''' + comb = {'rocket': [], 'bomb': [], 'trio': [], 'trio_chain': [], + 'solo_chain': [], 'pair_chain': [], 'pair': [], 'solo': []} + # 1. pick rocket + if hand[-2:] == 'BR': + comb['rocket'].append('BR') + hand = hand[:-2] + # 2. pick bomb + hand_cp = hand + for index in range(len(hand_cp) - 3): + if hand_cp[index] == hand_cp[index+3]: + bomb = hand_cp[index: index+4] + comb['bomb'].append(bomb) + hand = hand.replace(bomb, '') + # 3. pick trio and trio_chain + hand_cp = hand + for index in range(len(hand_cp) - 2): + if hand_cp[index] == hand_cp[index+2]: + trio = hand_cp[index: index+3] + if len(comb['trio']) > 0 and INDEX[trio[-1]] < 12 and (INDEX[trio[-1]]-1) == INDEX[comb['trio'][-1][-1]]: + comb['trio'][-1] += trio + else: + comb['trio'].append(trio) + hand = hand.replace(trio, '') + only_trio = [] + only_trio_chain = [] + for trio in comb['trio']: + if len(trio) == 3: + only_trio.append(trio) + else: + only_trio_chain.append(trio) + comb['trio'] = only_trio + comb['trio_chain'] = only_trio_chain + # 4. pick solo chain + hand_list = card_str2list(hand) + chains, hand_list = pick_chain(hand_list, 1) + comb['solo_chain'] = chains + # 5. pick par_chain + chains, hand_list = pick_chain(hand_list, 2) + comb['pair_chain'] = chains + hand = list2card_str(hand_list) + # 6. pick pair and solo + index = 0 + while index < len(hand) - 1: + if hand[index] == hand[index+1]: + comb['pair'].append(hand[index] + hand[index+1]) + index += 2 + else: + comb['solo'].append(hand[index]) + index += 1 + if index == (len(hand) - 1): + comb['solo'].append(hand[index]) + return comb diff --git a/douzero/evaluation/simulation.py b/douzero/evaluation/simulation.py new file mode 100644 index 0000000..bac5cbd --- /dev/null +++ b/douzero/evaluation/simulation.py @@ -0,0 +1,162 @@ +import multiprocessing as mp +import pickle +import douzero.env.env +from douzero.dmc.models import Model +from douzero.env.game import GameEnv +import torch +import numpy as np +import BidModel + +def load_card_play_models(card_play_model_path_dict): + players = {} + + for position in ['landlord', 'landlord_up', 'landlord_down']: + if card_play_model_path_dict[position] == 'rlcard': + from .rlcard_agent import RLCardAgent + players[position] = RLCardAgent(position) + elif card_play_model_path_dict[position] == 'random': + from .random_agent import RandomAgent + players[position] = RandomAgent() + else: + from .deep_agent import DeepAgent + players[position] = DeepAgent(position, card_play_model_path_dict[position]) + return players + +def mp_simulate(card_play_data_list, card_play_model_path_dict, q, output, bid_output, title): + players = load_card_play_models(card_play_model_path_dict) + EnvCard2RealCard = {3: '3', 4: '4', 5: '5', 6: '6', 7: '7', + 8: '8', 9: '9', 10: 'T', 11: 'J', 12: 'Q', + 13: 'K', 14: 'A', 17: '2', 20: 'X', 30: 'D'} + env = GameEnv(players) + bid_model = None + if bid_output: + model = Model(device=0) + bid_model = model.get_model("bidding") + bid_model_path = card_play_model_path_dict["landlord"].replace("landlord", "bidding") + weights = torch.load(bid_model_path) + bid_model.load_state_dict(weights) + bid_model.eval() + for idx, card_play_data in enumerate(card_play_data_list): + env.card_play_init(card_play_data) + if bid_output: + output = True + bid_results = [] + bid_values = [] + bid_info_list = [ + np.array([[-1,-1,-1], + [-1,-1,-1], + [-1,-1,-1], + [-1,-1,-1]]), + np.array([[0,0,0], + [-1,-1,-1], + [-1,-1,-1], + [-1,-1,-1]]), + np.array([[1,0,0], + [-1,-1,-1], + [-1,-1,-1], + [-1,-1,-1]]), + np.array([[0,0,0], + [0,0,0], + [-1,-1,-1], + [-1,-1,-1]]), + np.array([[0,0,1], + [1,0,0], + [-1,-1,-1], + [-1,-1,-1]]), + np.array([[0,1,0], + [0,0,1], + [1,0,0], + [-1,-1,-1]]), + ] + for bid_info in bid_info_list: + bid_obs = douzero.env.env._get_obs_for_bid(1, bid_info, card_play_data["landlord"]) + result = bid_model.forward(torch.tensor(bid_obs["z_batch"], device=torch.device("cuda:0")), torch.tensor(bid_obs["x_batch"], device=torch.device("cuda:0")), True) + values = result["values"] + bid = 1 if values[1] > values[0] else 0 + bid_results.append(bid) + bid_values.append(values[bid]) + result2 = BidModel.predict_env(card_play_data["landlord"]) + print("".join([EnvCard2RealCard[c] for c in card_play_data["landlord"]]), end="") + print(" bid: %i|%i%i|%i%i|%i (%.3f %.3f %.3f %.3f %.3f %.3f) %.1f" % (bid_results[0],bid_results[1],bid_results[2],bid_results[3],bid_results[4],bid_results[5],bid_values[0],bid_values[1],bid_values[2],bid_values[3],bid_values[4],bid_values[5], result2)) + if output and not bid_output: + print("\nStart ------- " + title) + print ("".join([EnvCard2RealCard[c] for c in card_play_data["landlord"]])) + print ("".join([EnvCard2RealCard[c] for c in card_play_data["landlord_down"]])) + print ("".join([EnvCard2RealCard[c] for c in card_play_data["landlord_up"]])) + # print(card_play_data) + count = 0 + while not env.game_over and not bid_output: + action = env.step() + if output: + if count % 3 == 2: + end = "\n" + else: + end = " " + if len(action) == 0: + print("Pass", end=end) + else: + print("".join([EnvCard2RealCard[c] for c in action]), end=end) + count+=1 + if idx % 10 == 0 and not bid_output: + print("\nindex", idx) + # print("End -------") + env.reset() + + q.put((env.num_wins['landlord'], + env.num_wins['farmer'], + env.num_scores['landlord'], + env.num_scores['farmer'] + )) + +def data_allocation_per_worker(card_play_data_list, num_workers): + card_play_data_list_each_worker = [[] for k in range(num_workers)] + for idx, data in enumerate(card_play_data_list): + card_play_data_list_each_worker[idx % num_workers].append(data) + + return card_play_data_list_each_worker + +def evaluate(landlord, landlord_up, landlord_down, eval_data, num_workers, output, output_bid, title): + + with open(eval_data, 'rb') as f: + card_play_data_list = pickle.load(f) + + card_play_data_list_each_worker = data_allocation_per_worker( + card_play_data_list, num_workers) + del card_play_data_list + + card_play_model_path_dict = { + 'landlord': landlord, + 'landlord_up': landlord_up, + 'landlord_down': landlord_down} + + num_landlord_wins = 0 + num_farmer_wins = 0 + num_landlord_scores = 0 + num_farmer_scores = 0 + + ctx = mp.get_context('spawn') + q = ctx.SimpleQueue() + processes = [] + for card_paly_data in card_play_data_list_each_worker: + + p = ctx.Process( + target=mp_simulate, + args=(card_paly_data, card_play_model_path_dict, q, output, output_bid, title)) + p.start() + processes.append(p) + + for p in processes: + p.join() + + for i in range(num_workers): + result = q.get() + num_landlord_wins += result[0] + num_farmer_wins += result[1] + num_landlord_scores += result[2] + num_farmer_scores += result[3] + + num_total_wins = num_landlord_wins + num_farmer_wins + print('WP results:') + print('landlord : Farmers - {} : {}'.format(num_landlord_wins / num_total_wins, num_farmer_wins / num_total_wins)) + print('ADP results:') + print('landlord : Farmers - {} : {}'.format(num_landlord_scores / num_total_wins, 2 * num_farmer_scores / num_total_wins)) diff --git a/douzero/radam/__init__.py b/douzero/radam/__init__.py new file mode 100644 index 0000000..cefdc8c --- /dev/null +++ b/douzero/radam/__init__.py @@ -0,0 +1 @@ +from .radam import RAdam, PlainRAdam, AdamW \ No newline at end of file diff --git a/douzero/radam/radam.py b/douzero/radam/radam.py new file mode 100644 index 0000000..6e22f51 --- /dev/null +++ b/douzero/radam/radam.py @@ -0,0 +1,244 @@ +import math +import torch +from torch.optim.optimizer import Optimizer + +class RAdam(Optimizer): + + def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=False): + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + + self.degenerated_to_sgd = degenerated_to_sgd + if isinstance(params, (list, tuple)) and len(params) > 0 and isinstance(params[0], dict): + for param in params: + if 'betas' in param and (param['betas'][0] != betas[0] or param['betas'][1] != betas[1]): + param['buffer'] = [[None, None, None] for _ in range(10)] + defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, buffer=[[None, None, None] for _ in range(10)]) + super(RAdam, self).__init__(params, defaults) + + def __setstate__(self, state): + super(RAdam, self).__setstate__(state) + + def step(self, closure=None): + + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + + for p in group['params']: + if p.grad is None: + continue + grad = p.grad.data.float() + if grad.is_sparse: + raise RuntimeError('RAdam does not support sparse gradients') + + p_data_fp32 = p.data.float() + + state = self.state[p] + + if len(state) == 0: + state['step'] = 0 + state['exp_avg'] = torch.zeros_like(p_data_fp32) + state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) + else: + state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32) + state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32) + + exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] + beta1, beta2 = group['betas'] + + exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) + exp_avg.mul_(beta1).add_(1 - beta1, grad) + + state['step'] += 1 + buffered = group['buffer'][int(state['step'] % 10)] + if state['step'] == buffered[0]: + N_sma, step_size = buffered[1], buffered[2] + else: + buffered[0] = state['step'] + beta2_t = beta2 ** state['step'] + N_sma_max = 2 / (1 - beta2) - 1 + N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) + buffered[1] = N_sma + + # more conservative since it's an approximated value + if N_sma >= 5: + step_size = math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step']) + elif self.degenerated_to_sgd: + step_size = 1.0 / (1 - beta1 ** state['step']) + else: + step_size = -1 + buffered[2] = step_size + + # more conservative since it's an approximated value + if N_sma >= 5: + if group['weight_decay'] != 0: + p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) + denom = exp_avg_sq.sqrt().add_(group['eps']) + p_data_fp32.addcdiv_(-step_size * group['lr'], exp_avg, denom) + p.data.copy_(p_data_fp32) + elif step_size > 0: + if group['weight_decay'] != 0: + p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) + p_data_fp32.add_(-step_size * group['lr'], exp_avg) + p.data.copy_(p_data_fp32) + + return loss + +class PlainRAdam(Optimizer): + + def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, degenerated_to_sgd=False): + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + + self.degenerated_to_sgd = degenerated_to_sgd + defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) + + super(PlainRAdam, self).__init__(params, defaults) + + def __setstate__(self, state): + super(PlainRAdam, self).__setstate__(state) + + def step(self, closure=None): + + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + + for p in group['params']: + if p.grad is None: + continue + grad = p.grad.data.float() + if grad.is_sparse: + raise RuntimeError('RAdam does not support sparse gradients') + + p_data_fp32 = p.data.float() + + state = self.state[p] + + if len(state) == 0: + state['step'] = 0 + state['exp_avg'] = torch.zeros_like(p_data_fp32) + state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) + else: + state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32) + state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32) + + exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] + beta1, beta2 = group['betas'] + + exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) + exp_avg.mul_(beta1).add_(1 - beta1, grad) + + state['step'] += 1 + beta2_t = beta2 ** state['step'] + N_sma_max = 2 / (1 - beta2) - 1 + N_sma = N_sma_max - 2 * state['step'] * beta2_t / (1 - beta2_t) + + + # more conservative since it's an approximated value + if N_sma >= 5: + if group['weight_decay'] != 0: + p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) + step_size = group['lr'] * math.sqrt((1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)) / (1 - beta1 ** state['step']) + denom = exp_avg_sq.sqrt().add_(group['eps']) + p_data_fp32.addcdiv_(-step_size, exp_avg, denom) + p.data.copy_(p_data_fp32) + elif self.degenerated_to_sgd: + if group['weight_decay'] != 0: + p_data_fp32.add_(-group['weight_decay'] * group['lr'], p_data_fp32) + step_size = group['lr'] / (1 - beta1 ** state['step']) + p_data_fp32.add_(-step_size, exp_avg) + p.data.copy_(p_data_fp32) + + return loss + + +class AdamW(Optimizer): + + def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, warmup = 0): + if not 0.0 <= lr: + raise ValueError("Invalid learning rate: {}".format(lr)) + if not 0.0 <= eps: + raise ValueError("Invalid epsilon value: {}".format(eps)) + if not 0.0 <= betas[0] < 1.0: + raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) + if not 0.0 <= betas[1] < 1.0: + raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) + + defaults = dict(lr=lr, betas=betas, eps=eps, + weight_decay=weight_decay, warmup = warmup) + super(AdamW, self).__init__(params, defaults) + + def __setstate__(self, state): + super(AdamW, self).__setstate__(state) + + def step(self, closure=None): + loss = None + if closure is not None: + loss = closure() + + for group in self.param_groups: + + for p in group['params']: + if p.grad is None: + continue + grad = p.grad.data.float() + if grad.is_sparse: + raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead') + + p_data_fp32 = p.data.float() + + state = self.state[p] + + if len(state) == 0: + state['step'] = 0 + state['exp_avg'] = torch.zeros_like(p_data_fp32) + state['exp_avg_sq'] = torch.zeros_like(p_data_fp32) + else: + state['exp_avg'] = state['exp_avg'].type_as(p_data_fp32) + state['exp_avg_sq'] = state['exp_avg_sq'].type_as(p_data_fp32) + + exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] + beta1, beta2 = group['betas'] + + state['step'] += 1 + + exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) + exp_avg.mul_(beta1).add_(1 - beta1, grad) + + denom = exp_avg_sq.sqrt().add_(group['eps']) + bias_correction1 = 1 - beta1 ** state['step'] + bias_correction2 = 1 - beta2 ** state['step'] + + if group['warmup'] > state['step']: + scheduled_lr = 1e-8 + state['step'] * group['lr'] / group['warmup'] + else: + scheduled_lr = group['lr'] + + step_size = scheduled_lr * math.sqrt(bias_correction2) / bias_correction1 + + if group['weight_decay'] != 0: + p_data_fp32.add_(-group['weight_decay'] * scheduled_lr, p_data_fp32) + + p_data_fp32.addcdiv_(-step_size, exp_avg, denom) + + p.data.copy_(p_data_fp32) + + return loss \ No newline at end of file diff --git a/evaluate.py b/evaluate.py new file mode 100644 index 0000000..5a838e4 --- /dev/null +++ b/evaluate.py @@ -0,0 +1,91 @@ +import os +import argparse + +from douzero.evaluation.simulation import evaluate + +if __name__ == '__main__': + parser = argparse.ArgumentParser( + 'Dou Dizhu Evaluation') + parser.add_argument('--landlord', type=str, + default='baselines/douzero_12/landlord_weights_39762328900.ckpt') + parser.add_argument('--landlord_up', type=str, + default='baselines/douzero_12/landlord_up_weights_39762328900.ckpt') + parser.add_argument('--landlord_down', type=str, + default='baselines/douzero_12/landlord_down_weights_39762328900.ckpt') + parser.add_argument('--eval_data', type=str, + default='eval_data_1000.pkl') + parser.add_argument('--num_workers', type=int, default=5) + parser.add_argument('--gpu_device', type=str, default='0') + parser.add_argument('--output', type=bool, default=True) + parser.add_argument('--bid', type=bool, default=True) + parser.add_argument('--title', type=str, default='New') + args = parser.parse_args() + args.output = True + args.bid = False + if args.output or args.bid: + args.num_workers = 1 + t = 3 + frame = 3085177900 + adp_frame = 2511184300 + # args.landlord = 'baselines/resnet_landlord_%i.ckpt' % frame + args.landlord_up = 'baselines/resnet_landlord_up_%i.ckpt' % frame + args.landlord_down = 'baselines/resnet_landlord_%i.ckpt' % frame + args.landlord = 'baselines/douzero_ADP/landlord.ckpt' + # args.landlord_up = 'baselines/douzero_ADP/landlord_up.ckpt' + # args.landlord_down = 'baselines/douzero_ADP/landlord_down.ckpt' + if t == 1: + args.landlord = 'baselines/resnet_landlord_%i.ckpt' % frame + args.landlord_up = 'baselines/douzero_ADP/landlord_up.ckpt' + args.landlord_down = 'baselines/douzero_ADP/landlord_down.ckpt' + elif t == 2: + args.landlord = 'baselines/douzero_ADP/landlord.ckpt' + args.landlord_up = 'baselines/resnet_landlord_up_%i.ckpt' % frame + args.landlord_down = 'baselines/resnet_landlord_down_%i.ckpt' % frame + elif t == 3: + args.landlord = 'baselines/resnet_landlord_%i.ckpt' % frame + args.landlord_up = 'baselines/resnet_landlord_up_%i.ckpt' % frame + args.landlord_down = 'baselines/resnet_landlord_down_%i.ckpt' % frame + elif t == 4: + args.landlord = 'baselines/douzero_ADP/landlord.ckpt' + args.landlord_up = 'baselines/douzero_ADP/landlord_up.ckpt' + args.landlord_down = 'baselines/douzero_ADP/landlord_down.ckpt' + elif t == 5: + args.landlord = 'baselines/douzero_WP/landlord.ckpt' + args.landlord_up = 'baselines/douzero_WP/landlord_up.ckpt' + args.landlord_down = 'baselines/douzero_WP/landlord_down.ckpt' + elif t == 6: + args.landlord = 'baselines/resnet_landlord_%i.ckpt' % frame + args.landlord_up = 'baselines/douzero_ADP/landlord_up_weights_%i.ckpt' % adp_frame + args.landlord_down = 'baselines/douzero_ADP/landlord_down_weights_%i.ckpt' % adp_frame + elif t == 7: + args.landlord = 'baselines/douzero_ADP/landlord_weights_%i.ckpt' % adp_frame + args.landlord_up = 'baselines/resnet_landlord_up_%i.ckpt' % frame + args.landlord_down = 'baselines/resnet_landlord_down_%i.ckpt' % frame + elif t == 8: + args.landlord = 'baselines/douzero_ADP/landlord_weights_%i.ckpt' % adp_frame + args.landlord_up = 'baselines/douzero_ADP/landlord_up_weights_%i.ckpt' % adp_frame + args.landlord_down = 'baselines/douzero_ADP/landlord_down_weights_%i.ckpt' % adp_frame + elif t == 9: + args.landlord = 'baselines/resnet_landlord_%i.ckpt' % frame + args.landlord_up = 'baselines/resnet_landlord_up_%i.ckpt' % adp_frame + args.landlord_down = 'baselines/resnet_landlord_down_%i.ckpt' % adp_frame + elif t == 10: + # landlord_down_weights_10777798400 + args.landlord = 'baselines/douzero_ADP/landlord.ckpt' + args.landlord_up = 'baselines/douzero_ADP/landlord_up_weights_%i.ckpt' % adp_frame + args.landlord_down = 'baselines/douzero_ADP/landlord_down_weights_%i.ckpt' % adp_frame + elif t == 11: + args.landlord = 'baselines/douzero_ADP/landlord_weights_%i.ckpt' % adp_frame + args.landlord_up = 'baselines/douzero_ADP/landlord_up.ckpt' + args.landlord_down = 'baselines/douzero_ADP/landlord_down.ckpt' + os.environ['KMP_DUPLICATE_LIB_OK'] = 'True' + os.environ["CUDA_VISIBLE_DEVICES"] = args.gpu_device + + evaluate(args.landlord, + args.landlord_up, + args.landlord_down, + args.eval_data, + args.num_workers, + args.output, + args.bid, + args.title) diff --git a/generate_eval_data.py b/generate_eval_data.py new file mode 100644 index 0000000..6d0ff2a --- /dev/null +++ b/generate_eval_data.py @@ -0,0 +1,47 @@ +import argparse +import pickle +import numpy as np + +deck = [] +for i in range(3, 15): + deck.extend([i for _ in range(4)]) +deck.extend([17 for _ in range(4)]) +deck.extend([20, 30]) + +def get_parser(): + parser = argparse.ArgumentParser(description='DouZero: random data generator') + parser.add_argument('--output', default='eval_data', type=str) + parser.add_argument('--num_games', default=10000, type=int) + return parser + +def generate(): + _deck = deck.copy() + np.random.shuffle(_deck) + card_play_data = {'landlord': _deck[:20], + 'landlord_up': _deck[20:37], + 'landlord_down': _deck[37:54], + 'three_landlord_cards': _deck[17:20], + } + for key in card_play_data: + card_play_data[key].sort() + return card_play_data + + +if __name__ == '__main__': + flags = get_parser().parse_args() + output_pickle = flags.output + '.pkl' + + print("output_pickle:", output_pickle) + print("generating data...") + + data = [] + for _ in range(flags.num_games): + data.append(generate()) + 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