参数调整
This commit is contained in:
parent
64bf792d15
commit
3fec4a6bc1
|
|
@ -3,3 +3,6 @@ baselines*/
|
|||
douzero_checkpoints/
|
||||
.vscode/
|
||||
*.pkl
|
||||
venv/
|
||||
.idea/
|
||||
*.egg-info/
|
||||
|
|
|
|||
|
|
@ -13,7 +13,7 @@ class LandlordLstmModel(nn.Module):
|
|||
def __init__(self):
|
||||
super().__init__()
|
||||
self.lstm = nn.LSTM(432, 128, batch_first=True)
|
||||
self.dense1 = nn.Linear(860 + 128, 1024)
|
||||
self.dense1 = nn.Linear(887 + 128, 1024)
|
||||
self.dense2 = nn.Linear(1024, 1024)
|
||||
self.dense3 = nn.Linear(1024, 768)
|
||||
self.dense4 = nn.Linear(768, 512)
|
||||
|
|
@ -48,7 +48,7 @@ class FarmerLstmModel(nn.Module):
|
|||
def __init__(self):
|
||||
super().__init__()
|
||||
self.lstm = nn.LSTM(432, 128, batch_first=True)
|
||||
self.dense1 = nn.Linear(1192 + 128, 1024)
|
||||
self.dense1 = nn.Linear(1219 + 128, 1024)
|
||||
self.dense2 = nn.Linear(1024, 1024)
|
||||
self.dense3 = nn.Linear(1024, 768)
|
||||
self.dense4 = nn.Linear(768, 512)
|
||||
|
|
@ -79,16 +79,16 @@ class FarmerLstmModel(nn.Module):
|
|||
action = torch.argmax(x,dim=0)[0]
|
||||
return dict(action=action)
|
||||
|
||||
class LandlordLstmNewModel(nn.Module):
|
||||
class LandlordLstmModelLegacy(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.lstm = nn.LSTM(432, 128, batch_first=True)
|
||||
self.dense1 = nn.Linear(846 + 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)
|
||||
self.dense1 = nn.Linear(860 + 128, 1024)
|
||||
self.dense2 = nn.Linear(1024, 1024)
|
||||
self.dense3 = nn.Linear(1024, 768)
|
||||
self.dense4 = nn.Linear(768, 512)
|
||||
self.dense5 = nn.Linear(512, 256)
|
||||
self.dense6 = nn.Linear(256, 1)
|
||||
|
||||
def forward(self, z, x, return_value=False, flags=None):
|
||||
lstm_out, (h_n, _) = self.lstm(z)
|
||||
|
|
@ -114,16 +114,16 @@ class LandlordLstmNewModel(nn.Module):
|
|||
action = torch.argmax(x,dim=0)[0]
|
||||
return dict(action=action)
|
||||
|
||||
class FarmerLstmNewModel(nn.Module):
|
||||
class FarmerLstmModelLegacy(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.lstm = nn.LSTM(432, 128, batch_first=True)
|
||||
self.dense1 = nn.Linear(1178 + 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)
|
||||
self.dense1 = nn.Linear(1192 + 128, 1024)
|
||||
self.dense2 = nn.Linear(1024, 1024)
|
||||
self.dense3 = nn.Linear(1024, 768)
|
||||
self.dense4 = nn.Linear(768, 512)
|
||||
self.dense5 = nn.Linear(512, 256)
|
||||
self.dense6 = nn.Linear(256, 1)
|
||||
|
||||
def forward(self, z, x, return_value=False, flags=None):
|
||||
lstm_out, (h_n, _) = self.lstm(z)
|
||||
|
|
@ -249,11 +249,10 @@ class BasicBlock(nn.Module):
|
|||
return out
|
||||
|
||||
|
||||
class GeneralModel(nn.Module):
|
||||
def __init__(self, use_legacy = False):
|
||||
class GeneralModelLegacy(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.in_planes = 80
|
||||
self.use_legacy = use_legacy
|
||||
#input 1*108*41
|
||||
self.conv1 = nn.Conv1d(40, 80, kernel_size=(3,),
|
||||
stride=(2,), padding=1, bias=False) #1*108*80
|
||||
|
|
@ -265,10 +264,7 @@ class GeneralModel(nn.Module):
|
|||
self.layer3 = self._make_layer(BasicBlock, 320, 2, stride=2)#1*7*320
|
||||
self.layer4 = self._make_layer(BasicBlock, 640, 2, stride=2)#1*4*640
|
||||
# self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
|
||||
if self.use_legacy:
|
||||
self.linear1 = nn.Linear(640 * BasicBlock.expansion * 4 + 24 * 4, 2048)
|
||||
else:
|
||||
self.linear1 = nn.Linear(640 * BasicBlock.expansion * 4 + 80, 2048)
|
||||
self.linear1 = nn.Linear(640 * BasicBlock.expansion * 4 + 24 * 4, 2048)
|
||||
self.linear2 = nn.Linear(2048, 1024)
|
||||
self.linear3 = nn.Linear(1024, 512)
|
||||
self.linear4 = nn.Linear(512, 256)
|
||||
|
|
@ -289,10 +285,58 @@ class GeneralModel(nn.Module):
|
|||
out = self.layer3(out)
|
||||
out = self.layer4(out)
|
||||
out = out.flatten(1,2)
|
||||
if self.use_legacy:
|
||||
out = torch.cat([x,x,x,x,out], dim=-1)
|
||||
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))
|
||||
out = F.leaky_relu_(self.linear5(out))
|
||||
if return_value:
|
||||
return dict(values=out)
|
||||
else:
|
||||
out = torch.cat([x,out], dim=-1)
|
||||
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 GeneralModel(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.in_planes = 80
|
||||
#input 1*108*41
|
||||
self.conv1 = nn.Conv1d(40, 80, kernel_size=(3,),
|
||||
stride=(2,), padding=1, bias=False) #1*108*80
|
||||
|
||||
self.bn1 = nn.BatchNorm1d(80)
|
||||
|
||||
self.layer1 = self._make_layer(BasicBlock, 80, 2, stride=2)#1*27*80
|
||||
self.layer2 = self._make_layer(BasicBlock, 160, 2, stride=2)#1*14*160
|
||||
self.layer3 = self._make_layer(BasicBlock, 320, 2, stride=2)#1*7*320
|
||||
self.layer4 = self._make_layer(BasicBlock, 640, 2, stride=2)#1*4*640
|
||||
# self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
|
||||
self.linear1 = nn.Linear(640 * BasicBlock.expansion * 4 + 80, 2048)
|
||||
self.linear2 = nn.Linear(2048, 1024)
|
||||
self.linear3 = nn.Linear(1024, 512)
|
||||
self.linear4 = nn.Linear(512, 256)
|
||||
self.linear5 = 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 = self.layer4(out)
|
||||
out = out.flatten(1,2)
|
||||
out = torch.cat([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))
|
||||
|
|
@ -355,6 +399,17 @@ model_dict['landlord'] = LandlordLstmModel
|
|||
model_dict['landlord_up'] = FarmerLstmModel
|
||||
model_dict['landlord_front'] = FarmerLstmModel
|
||||
model_dict['landlord_down'] = FarmerLstmModel
|
||||
model_dict_legacy = {}
|
||||
model_dict_legacy['landlord'] = LandlordLstmModelLegacy
|
||||
model_dict_legacy['landlord_up'] = FarmerLstmModelLegacy
|
||||
model_dict_legacy['landlord_front'] = FarmerLstmModelLegacy
|
||||
model_dict_legacy['landlord_down'] = FarmerLstmModelLegacy
|
||||
model_dict_new_legacy = {}
|
||||
model_dict_new_legacy['landlord'] = GeneralModelLegacy
|
||||
model_dict_new_legacy['landlord_up'] = GeneralModelLegacy
|
||||
model_dict_new_legacy['landlord_front'] = GeneralModelLegacy
|
||||
model_dict_new_legacy['landlord_down'] = GeneralModelLegacy
|
||||
model_dict_new_legacy['bidding'] = BidModel
|
||||
model_dict_new = {}
|
||||
model_dict_new['landlord'] = GeneralModel
|
||||
model_dict_new['landlord_up'] = GeneralModel
|
||||
|
|
@ -458,15 +513,15 @@ class Model:
|
|||
The wrapper for the three models. We also wrap several
|
||||
interfaces such as share_memory, eval, etc.
|
||||
"""
|
||||
def __init__(self, device=0, use_legacy = False):
|
||||
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(use_legacy).to(torch.device(device))
|
||||
self.models['landlord_up'] = GeneralModel(use_legacy).to(torch.device(device))
|
||||
self.models['landlord_front'] = GeneralModel(use_legacy).to(torch.device(device))
|
||||
self.models['landlord_down'] = GeneralModel(use_legacy).to(torch.device(device))
|
||||
self.models['landlord'] = GeneralModel().to(torch.device(device))
|
||||
self.models['landlord_up'] = GeneralModel().to(torch.device(device))
|
||||
self.models['landlord_front'] = 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):
|
||||
|
|
|
|||
|
|
@ -3,13 +3,19 @@ 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
|
||||
def _load_model(position, model_path, model_type, use_legacy):
|
||||
from douzero.dmc.models import model_dict_new, model_dict, model_dict_new_legacy, model_dict_legacy
|
||||
model = None
|
||||
if model_type == "general":
|
||||
model = model_dict_new[position]()
|
||||
if use_legacy:
|
||||
model = model_dict_new_legacy[position]()
|
||||
else:
|
||||
model = model_dict_new[position]()
|
||||
else:
|
||||
model = model_dict[position]()
|
||||
if use_legacy:
|
||||
model = model_dict_legacy[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')
|
||||
|
|
@ -27,8 +33,9 @@ def _load_model(position, model_path, model_type):
|
|||
class DeepAgent:
|
||||
|
||||
def __init__(self, position, model_path):
|
||||
self.use_legacy = True if "legacy" in model_path else False
|
||||
self.model_type = "general" if "resnet" in model_path else "old"
|
||||
self.model = _load_model(position, model_path, self.model_type)
|
||||
self.model = _load_model(position, model_path, self.model_type, self.use_legacy)
|
||||
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'}
|
||||
|
|
@ -36,7 +43,7 @@ class DeepAgent:
|
|||
if len(infoset.legal_actions) == 1:
|
||||
return infoset.legal_actions[0]
|
||||
|
||||
obs = get_obs(infoset, self.model_type == "general")
|
||||
obs = get_obs(infoset, self.model_type == "general", self.use_legacy)
|
||||
|
||||
z_batch = torch.from_numpy(obs['z_batch']).float()
|
||||
x_batch = torch.from_numpy(obs['x_batch']).float()
|
||||
|
|
|
|||
32
evaluate.py
32
evaluate.py
|
|
@ -4,7 +4,7 @@ import argparse
|
|||
from douzero.evaluation.simulation import evaluate
|
||||
|
||||
|
||||
def make_evaluate(args, t, frame, adp_frame, folder_a = 'baselines', folder_b = 'baselines'):
|
||||
def make_evaluate(args, t, frame, adp_frame, folder_a = 'baselines', folder_b = 'baselines', prefix_a = '', prefix_b = ''):
|
||||
if t == 0:
|
||||
args.landlord = 'random'
|
||||
args.landlord_up = 'random'
|
||||
|
|
@ -24,16 +24,16 @@ def make_evaluate(args, t, frame, adp_frame, folder_a = 'baselines', folder_b =
|
|||
args.landlord_down = '%s/resnet_landlord_down_%i.ckpt' % (folder_a, frame)
|
||||
print('random vs %i' % frame)
|
||||
elif t == 3:
|
||||
args.landlord = '%s/resnet_landlord_%i.ckpt' % (folder_a, frame)
|
||||
args.landlord_up = '%s/resnet_landlord_up_%i.ckpt' % (folder_b, adp_frame)
|
||||
args.landlord_front = '%s/resnet_landlord_front_%i.ckpt' % (folder_b, adp_frame)
|
||||
args.landlord_down = '%s/resnet_landlord_down_%i.ckpt' % (folder_b, adp_frame)
|
||||
args.landlord = '%s/%sresnet_landlord_%i.ckpt' % (folder_a, prefix_a, frame)
|
||||
args.landlord_up = '%s/%sresnet_landlord_up_%i.ckpt' % (folder_b, prefix_b, adp_frame)
|
||||
args.landlord_front = '%s/%sresnet_landlord_front_%i.ckpt' % (folder_b, prefix_b, adp_frame)
|
||||
args.landlord_down = '%s/%sresnet_landlord_down_%i.ckpt' % (folder_b, prefix_b, adp_frame)
|
||||
print('%i vs %i' % (frame, adp_frame))
|
||||
elif t == 4:
|
||||
args.landlord = '%s/resnet_landlord_%i.ckpt' % (folder_b, adp_frame)
|
||||
args.landlord_up = '%s/resnet_landlord_up_%i.ckpt' % (folder_a, frame)
|
||||
args.landlord_front = '%s/resnet_landlord_front_%i.ckpt' % (folder_a, frame)
|
||||
args.landlord_down = '%s/resnet_landlord_down_%i.ckpt' % (folder_a, frame)
|
||||
args.landlord = '%s/%sresnet_landlord_%i.ckpt' % (folder_b, prefix_b, adp_frame)
|
||||
args.landlord_up = '%s/%sresnet_landlord_up_%i.ckpt' % (folder_a, prefix_a, frame)
|
||||
args.landlord_front = '%s/%sresnet_landlord_front_%i.ckpt' % (folder_a, prefix_a, frame)
|
||||
args.landlord_down = '%s/%sresnet_landlord_down_%i.ckpt' % (folder_a, prefix_a, frame)
|
||||
print('%i vs %i' % (adp_frame, frame))
|
||||
|
||||
evaluate(args.landlord,
|
||||
|
|
@ -108,8 +108,10 @@ if __name__ == '__main__':
|
|||
# [14102400, 4968800, 'baselines', 'baselines'],
|
||||
# [14102400, 13252000, 'baselines', 'baselines2'],
|
||||
# [14102400, 15096800, 'baselines', 'baselines2'],
|
||||
[34828000, 40132800, 'baselines2', 'baselines2'],
|
||||
# [34828000, 40132800, 'baselines2', 'baselines2'],
|
||||
# [14102400, None, 'baselines', 'baselines'],
|
||||
[19918400, 19918400, 'baselines', 'baselines', 'legacy_', 'legacy_'],
|
||||
[9161600, 19918400, 'baselines', 'baselines', '', 'legacy_'],
|
||||
]
|
||||
|
||||
for vs in reversed(eval_list):
|
||||
|
|
@ -119,11 +121,11 @@ if __name__ == '__main__':
|
|||
folder_b = vs[3]
|
||||
if adp_frame is None:
|
||||
if frame is None:
|
||||
make_evaluate(args, 0, None, None)
|
||||
make_evaluate(args, 0, None, None, folder_a , folder_b, vs[4], vs[5])
|
||||
else:
|
||||
make_evaluate(args, 1, frame, None)
|
||||
make_evaluate(args, 2, frame, None)
|
||||
make_evaluate(args, 1, frame, None, folder_a , folder_b, vs[4], vs[5])
|
||||
make_evaluate(args, 2, frame, None, folder_a , folder_b, vs[4], vs[5])
|
||||
else:
|
||||
make_evaluate(args, 3, frame, adp_frame, folder_a , folder_b)
|
||||
make_evaluate(args, 3, frame, adp_frame, folder_a , folder_b, vs[4], vs[5])
|
||||
if frame != adp_frame:
|
||||
make_evaluate(args, 4, frame, adp_frame, folder_a, folder_b)
|
||||
make_evaluate(args, 4, frame, adp_frame, folder_a, folder_b, vs[4], vs[5])
|
||||
|
|
|
|||
Loading…
Reference in New Issue