移除根据胜率叫地主逻辑（4人场景下，胜率计算未适配）
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zhiyang7
parent
a755ffe719
commit
c239085c24
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@ -129,11 +129,11 @@ class Env:
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with torch.no_grad():
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action = model.forward("bidding", torch.tensor(bidding_obs["z_batch"], device=device),
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torch.tensor(bidding_obs["x_batch"], device=device), flags=flags)
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if bid_limit <= 0:
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wr = BidModel.predict_env(card_play_data[bidding_player])
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if wr >= 0.7:
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action = {"action": 1} # debug
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bid_limit += 1
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# if bid_limit <= 0:
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# wr = BidModel.predict_env(card_play_data[bidding_player])
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# if wr >= 0.7:
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# action = {"action": 1} # debug
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# bid_limit += 1
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bid_obs_buffer.append({
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"x_batch": bidding_obs["x_batch"][action["action"]],
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18
evaluate.py
18
evaluate.py
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@ -5,6 +5,12 @@ from douzero.evaluation.simulation import evaluate
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def make_evaluate(args, t, frame, adp_frame, folder_a = 'baselines', folder_b = 'baselines'):
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if t == 0:
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args.landlord = 'random'
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args.landlord_up = 'random'
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args.landlord_front = 'random'
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args.landlord_down = 'random'
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print('random vs random')
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if t == 1:
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args.landlord = '%s/resnet_landlord_%i.ckpt' % (folder_a, frame)
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args.landlord_up = 'random'
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@ -96,8 +102,13 @@ if __name__ == '__main__':
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# ]
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eval_list = [
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[4968800, 8697600, 'baselines', 'baselines2'],
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[4968800, 4968800, 'baselines', 'baselines'],
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# [4968800, 8697600, 'baselines', 'baselines2'],
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# [4968800, 4968800, 'baselines', 'baselines'],
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# [14102400, 4968800, 'baselines', 'baselines'],
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# [14102400, 13252000, 'baselines', 'baselines2'],
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# [14102400, 15096800, 'baselines', 'baselines2'],
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[14102400, 14102400, 'baselines', 'baselines'],
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# [14102400, None, 'baselines', 'baselines'],
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]
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for vs in reversed(eval_list):
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@ -106,6 +117,9 @@ if __name__ == '__main__':
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folder_a = vs[2]
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folder_b = vs[3]
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if adp_frame is None:
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if frame is None:
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make_evaluate(args, 0, None, None)
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else:
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make_evaluate(args, 1, frame, None)
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make_evaluate(args, 2, frame, None)
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else:
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@ -0,0 +1,202 @@
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import argparse
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import pickle
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import numpy as np
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import torch
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import random
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import douzero
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from douzero.dmc.models import Model
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deck = []
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for i in range(3, 15):
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deck.extend([i for _ in range(8)])
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deck.extend([17 for _ in range(8)])
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deck.extend([20, 20, 30, 30])
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def get_parser():
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parser = argparse.ArgumentParser(description='DouZero: random data generator')
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parser.add_argument('--output', default='eval_data', type=str)
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parser.add_argument('--path', default='baselines/resnet_bidding_15419200.ckpt', type=str)
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parser.add_argument('--num_games', default=10000, type=int)
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parser.add_argument('--exp_epsilon', default=0.01, type=float)
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return parser
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def generate_with_bid(num_games, bid_model_path):
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data_list = []
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for i in range(num_games):
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bid_done = False
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card_play_data = []
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landlord_cards = []
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last_bid = 0
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bid_count = 0
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player_ids = {}
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bid_info = None
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bid_obs_buffer = []
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multiply_obs_buffer = []
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bid_limit = 4
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force_bid = False
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device = torch.device("cpu")
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model = Model(device='cpu')
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bid_model = model.get_model("bidding")
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weights = torch.load(bid_model_path, map_location=device)
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bid_model.load_state_dict(weights)
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bid_model.eval()
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while not bid_done:
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bid_limit -= 1
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bid_obs_buffer.clear()
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multiply_obs_buffer.clear()
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_deck = deck.copy()
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np.random.shuffle(_deck)
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card_play_data = [
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_deck[:25],
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_deck[25:50],
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_deck[50:75],
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_deck[75:100],
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]
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for i in range(4):
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card_play_data[i].sort()
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landlord_cards = _deck[100:108]
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landlord_cards.sort()
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bid_info = np.array([[-1, -1, -1, -1],
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[-1, -1, -1, -1],
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[-1, -1, -1, -1],
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[-1, -1, -1, -1],
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[-1, -1, -1, -1]])
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bidding_player = random.randint(0, 3)
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# bidding_player = 0 # debug
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first_bid = -1
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last_bid = -1
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bid_count = 0
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for r in range(4):
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bidding_obs = douzero.env.env._get_obs_for_bid(bidding_player, bid_info, card_play_data[bidding_player])
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with torch.no_grad():
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action = model.forward("bidding", torch.tensor(bidding_obs["z_batch"], device=device),
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torch.tensor(bidding_obs["x_batch"], device=device), flags=flags)
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# if bid_limit <= 0:
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# if random.random() < 0.5:
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# action = {"action": 1} # debug
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# bid_limit += 1
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# if bid_count == 0:
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# bid_score, farmer_score = BidModel.predict_env(card_play_data[bidding_player])
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# if bid_score * 3 > farmer_score or bid_score > 0:
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# action = {"action": 1} # debug
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# bid_limit += 1
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# else:
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# action = {"action": 0}
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# else:
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# bid_score, farmer_score = BidModel.predict_env(card_play_data[bidding_player])
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# if bid_score * 2.8 > farmer_score or bid_score > 0.1:
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# action = {"action": 1} # debug
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# bid_limit += 1
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# else:
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# action = {"action": 0}
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# bid_obs_buffer.append({
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# "x_batch": bidding_obs["x_batch"][0],
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# "z_batch": bidding_obs["z_batch"][0],
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# "action": action["action"],
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# "pid": bidding_player
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# })
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if action["action"] == 1:
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last_bid = bidding_player
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bid_count += 1
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if first_bid == -1:
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first_bid = bidding_player
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for p in range(4):
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if p == bidding_player:
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bid_info[r][p] = 1
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else:
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bid_info[r][p] = 0
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else:
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bid_info[r] = [0, 0, 0, 0]
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bidding_player = (bidding_player + 1) % 4
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one_count = np.count_nonzero(bid_info == 1)
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if one_count == 0:
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continue
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elif one_count > 1:
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r = 4
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bidding_player = first_bid
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bidding_obs = douzero.env.env._get_obs_for_bid(bidding_player, bid_info, card_play_data[bidding_player])
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with torch.no_grad():
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action = model.forward("bidding", torch.tensor(bidding_obs["z_batch"], device=device),
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torch.tensor(bidding_obs["x_batch"], device=device), flags=flags)
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# bid_score, farmer_score = BidModel.predict_env(card_play_data[bidding_player])
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# if bid_score * 2.9 > farmer_score or bid_score > 0.1:
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# action = {"action": 1} # debug
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# bid_limit += 1
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# else:
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# action = {"action": 0}
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bid_obs_buffer.append({
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"x_batch": bidding_obs["x_batch"][action["action"]],
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"z_batch": bidding_obs["z_batch"][action["action"]],
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"pid": bidding_player
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})
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if action["action"] == 1:
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last_bid = bidding_player
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bid_count += 1
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for p in range(4):
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if p == bidding_player:
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bid_info[r][p] = 1
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else:
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bid_info[r][p] = 0
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break
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card_play_data[last_bid].extend(landlord_cards)
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card_play_data = {'landlord': card_play_data[last_bid],
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'landlord_up': card_play_data[(last_bid - 1) % 4],
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'landlord_front': card_play_data[(last_bid + 2) % 4],
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'landlord_down': card_play_data[(last_bid + 1) % 4],
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# 'three_landlord_cards': landlord_cards,
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}
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card_play_data["landlord"].sort()
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player_ids = {
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'landlord': last_bid,
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'landlord_up': (last_bid - 1) % 4,
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'landlord_down': (last_bid + 1) % 4,
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'landlord_front': (last_bid + 2) % 4,
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}
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player_positions = {
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last_bid: 'landlord',
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(last_bid - 1) % 4: 'landlord_up',
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(last_bid + 1) % 4: 'landlord_down',
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(last_bid + 2) % 4: 'landlord_front',
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}
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for bid_obs in bid_obs_buffer:
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bid_obs.update({"position": player_positions[bid_obs["pid"]]})
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bid_info_list = {}
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for pos in ["landlord", "landlord_up", "landlord_front", "landlord_down"]:
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pid = player_ids[pos]
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bid_info_list[pos] = bid_info[:, [(pid - 1) % 4, pid, (pid + 1) % 4, (pid + 2) % 4]]
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card_play_data = {
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"play": card_play_data,
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"bid": bid_info_list
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}
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data_list.append(card_play_data)
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return data_list
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if __name__ == '__main__':
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flags = get_parser().parse_args()
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output_pickle = flags.output + '.pkl'
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print("output_pickle:", output_pickle)
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print("generating data...")
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data = []
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data.extend(generate_with_bid(flags.num_games, flags.path))
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# round_count = flags.num_games // 3
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# for _ in range(round_count):
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# data.extend(generate_3())
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# if round_count * 3 < flags.num_games:
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# for i in range(flags.num_games - round_count*3):
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# data.extend(generate_1())
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print(data)
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print("saving pickle file...")
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with open(output_pickle,'wb') as g:
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pickle.dump(data,g)
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