unified_model
This commit is contained in:
zhiyang7
parent
2b8586303b
commit
08e05dbc83
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@ -31,6 +31,8 @@ parser.add_argument('--load_model', action='store_true',
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help='Load an existing model')
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parser.add_argument('--old_model', action='store_true',
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help='Use vanilla model')
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parser.add_argument('--unified_model', action='store_true',
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help='Use unified model')
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parser.add_argument('--lite_model', action='store_true',
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help='Use lite card model')
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parser.add_argument('--lagacy_model', action='store_true',
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@ -13,8 +13,8 @@ from torch import nn
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import douzero.dmc.models
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import douzero.env.env
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from .file_writer import FileWriter
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from .models import Model, OldModel
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from .utils import get_batch, log, create_env, create_optimizers, act, infer_logic
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from .models import Model, OldModel, UnifiedModel
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from .utils import get_batch, log, create_optimizers, act, infer_logic
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import psutil
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import shutil
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import requests
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@ -87,6 +87,8 @@ def train(flags):
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# Initialize actor models
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if flags.old_model:
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actor_model = OldModel(device="cpu", flags = flags, lite_model = flags.lite_model)
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elif flags.unified_model:
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actor_model = UnifiedModel(device="cpu", flags = flags, lite_model = flags.lite_model)
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else:
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actor_model = Model(device="cpu", flags = flags, lite_model = flags.lite_model)
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actor_model.eval()
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@ -100,6 +102,8 @@ def train(flags):
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# Learner model for training
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if flags.old_model:
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learner_model = OldModel(device=flags.training_device, lite_model = flags.lite_model)
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elif flags.unified_model:
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learner_model = UnifiedModel(device=flags.training_device, lite_model = flags.lite_model)
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else:
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learner_model = Model(device=flags.training_device, lite_model = flags.lite_model)
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@ -255,6 +259,8 @@ def train(flags):
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type = ''
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if flags.old_model:
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type += 'vanilla'
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elif flags.unified_model:
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type += 'unified'
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else:
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type += 'resnet'
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requests.post(flags.upload_url, data={
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@ -400,6 +400,74 @@ class GeneralModelLite(nn.Module):
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return dict(values=out)
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class UnifiedModelLite(nn.Module):
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def __init__(self):
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super().__init__()
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self.in_planes = 30
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#input 1*69*15
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self.conv1 = nn.Conv1d(15, 30, kernel_size=(3,),
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stride=(2,), padding=1, bias=False) #1*35*30
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self.bn1 = nn.BatchNorm1d(30)
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self.layer1 = self._make_layer(BasicBlock, 30, 2, stride=2)#1*18*30
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self.layer2 = self._make_layer(BasicBlock, 60, 2, stride=2)#1*9*60
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self.layer3 = self._make_layer(BasicBlock, 120, 2, stride=2)#1*5*120
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# self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
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self.lstm = nn.LSTM(276, 128, batch_first=True)
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self.linear1 = nn.Linear(120 * BasicBlock.expansion * 5 + 128, 2048)
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self.linear2 = nn.Linear(2048, 1024)
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self.linear3 = nn.Linear(1024, 512)
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self.linear4 = nn.Linear(512, 256)
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self.linear5 = nn.Linear(256, 1)
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def _make_layer(self, block, planes, num_blocks, stride):
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strides = [stride] + [1] * (num_blocks - 1)
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layers = []
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for stride in strides:
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layers.append(block(self.in_planes, planes, stride))
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self.in_planes = planes * block.expansion
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return nn.Sequential(*layers)
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def get_onnx_params(self, device=None):
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return {
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'args': (
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torch.randn(1, 40, 69, requires_grad=True, device=device),
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torch.randn(1, 56, requires_grad=True, device=device),
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),
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'input_names': ['z_batch','x_batch'],
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'output_names': ['values'],
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'dynamic_axes': {
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'z_batch': {
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0: "batch_size"
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},
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'x_batch': {
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0: "batch_size"
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},
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'values': {
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0: "batch_size"
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}
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}
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}
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def forward(self, z, x):
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out = F.relu(self.bn1(self.conv1(z)))
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out = self.layer1(out)
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out = self.layer2(out)
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out = self.layer3(out)
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out = out.flatten(1,2)
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lstm_out, (h_n, _) = self.lstm(x)
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lstm_out = lstm_out[:,-1,:]
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out = torch.hstack([lstm_out, out])
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out = F.leaky_relu_(self.linear1(out))
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out = F.leaky_relu_(self.linear2(out))
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out = F.leaky_relu_(self.linear3(out))
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out = F.leaky_relu_(self.linear4(out))
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out = F.leaky_relu_(self.linear5(out))
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return dict(values=out)
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class GeneralModel(nn.Module):
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def __init__(self):
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super().__init__()
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@ -675,3 +743,61 @@ class Model:
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def get_models(self):
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return self.models
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class UnifiedModel:
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"""
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The wrapper for the three models. We also wrap several
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interfaces such as share_memory, eval, etc.
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"""
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def __init__(self, device=0, flags=None, lite_model = False):
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self.onnx_models = {}
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self.model = None
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self.models = {}
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self.flags = flags
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if not device == "cpu":
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device = 'cuda:' + str(device)
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self.device = torch.device(device)
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positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
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if flags is not None and flags.enable_onnx:
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self.model = None
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else:
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if lite_model:
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self.model = UnifiedModelLite().to(self.device)
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for position in positions:
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self.models[position] = self.model
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else:
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self.model = GeneralModel().to(self.device)
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self.onnx_model = None
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def set_onnx_model(self, device='cpu'):
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model_path = os.path.abspath('%s/%s/model_%s.onnx' % (self.flags.onnx_model_path, self.flags.xpid))
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if device == 'cpu':
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self.onnx_model = onnxruntime.InferenceSession(get_example(model_path), providers=['CPUExecutionProvider'])
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else:
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self.onnx_model = onnxruntime.InferenceSession(get_example(model_path), providers=['CUDAExecutionProvider'])
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def get_onnx_params(self, position):
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self.model.get_onnx_params(self.device)
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def forward(self, position, z, x, device='cpu', return_value=False, flags=None):
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return forward_logic(self, position, z, x, device, return_value, flags)
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def share_memory(self):
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if self.model is not None:
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self.model.share_memory()
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def eval(self):
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if self.model is not None:
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self.model.eval()
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def parameters(self, position):
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return self.model.parameters()
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def get_model(self, position):
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return self.model
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def get_models(self):
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return {
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'uni' : self.model
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}
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@ -73,7 +73,7 @@ log.setLevel(logging.INFO)
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Buffers = typing.Dict[str, typing.List[torch.Tensor]]
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def create_env(flags):
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return Env(flags.objective, flags.old_model, flags.lagacy_model, flags.lite_model)
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return Env(flags.objective, flags.old_model, flags.lagacy_model, flags.lite_model, flags.unified_model)
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def get_batch(b_queues, position, flags, lock):
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"""
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@ -116,8 +116,11 @@ def create_optimizers(flags, learner_model):
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def infer_logic(i, device, infer_queues, model, flags, onnx_frame):
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positions = ['landlord', 'landlord_up', 'landlord_front', 'landlord_down']
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if not flags.enable_onnx:
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for pos in positions:
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model.models[pos].to(torch.device(device if device == "cpu" else ("cuda:"+str(device))))
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if flags.unified_model:
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model.model.to(torch.device(device if device == "cpu" else ("cuda:"+str(device))))
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else:
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for pos in positions:
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model.models[pos].to(torch.device(device if device == "cpu" else ("cuda:"+str(device))))
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last_onnx_frame = -1
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log.info('Infer %i started.', i)
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@ -49,7 +49,47 @@ NumOnesJoker2ArrayCompressed = {0: np.array([0, 0, 0, 0, 0]),
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12: np.array([0, 0, 1, 1, 0]),
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13: np.array([1, 0, 1, 1, 0]),
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15: np.array([1, 1, 1, 1, 0])}
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PositionInfoArray = {
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'landlord': np.array([1, 0, 0, 0]),
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'landlord_down': np.array([0, 1, 0, 0]),
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'landlord_front': np.array([0, 0, 1, 0]),
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'landlord_up': np.array([0, 0, 0, 1]),
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}
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FaceUpLevelArray = {
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0x00: np.array([0, 0, 0, 0, 0, 0, 0, 0, 0]),
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0x01: np.array([1, 0, 0, 0, 0, 0, 0, 0, 0]),
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0x02: np.array([0, 1, 0, 0, 0, 0, 0, 0, 0]),
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0x03: np.array([1, 1, 0, 0, 0, 0, 0, 0, 0]),
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0x04: np.array([0, 0, 1, 0, 0, 0, 0, 0, 0]),
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0x05: np.array([1, 0, 1, 0, 0, 0, 0, 0, 0]),
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0x06: np.array([0, 1, 1, 0, 0, 0, 0, 0, 0]),
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0x07: np.array([1, 1, 1, 0, 0, 0, 0, 0, 0]),
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0x08: np.array([0, 0, 0, 1, 0, 0, 0, 0, 0]),
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0x09: np.array([1, 0, 0, 1, 0, 0, 0, 0, 0]),
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0x0A: np.array([0, 1, 0, 1, 0, 0, 0, 0, 0]),
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0x0B: np.array([1, 1, 0, 1, 0, 0, 0, 0, 0]),
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0x0C: np.array([0, 0, 1, 1, 0, 0, 0, 0, 0]),
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0x0D: np.array([1, 0, 1, 1, 0, 0, 0, 0, 0]),
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0x0E: np.array([0, 1, 1, 1, 0, 0, 0, 0, 0]),
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0x0F: np.array([1, 1, 1, 1, 0, 0, 0, 0, 0]),
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0x10: np.array([0, 0, 0, 0, 1, 0, 0, 0, 0]),
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0x11: np.array([1, 0, 0, 0, 1, 0, 0, 0, 0]),
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0x12: np.array([0, 1, 0, 0, 1, 0, 0, 0, 0]),
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0x13: np.array([1, 1, 0, 0, 1, 0, 0, 0, 0]),
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0x14: np.array([0, 0, 1, 0, 1, 0, 0, 0, 0]),
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0x15: np.array([1, 0, 1, 0, 1, 0, 0, 0, 0]),
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0x16: np.array([0, 1, 1, 0, 1, 0, 0, 0, 0]),
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0x17: np.array([1, 1, 1, 0, 1, 0, 0, 0, 0]),
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0x18: np.array([0, 0, 0, 1, 1, 0, 0, 0, 0]),
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0x19: np.array([1, 0, 0, 1, 1, 0, 0, 0, 0]),
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0x1A: np.array([0, 1, 0, 1, 1, 0, 0, 0, 0]),
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0x1B: np.array([1, 1, 0, 1, 1, 0, 0, 0, 0]),
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0x1C: np.array([0, 0, 1, 1, 1, 0, 0, 0, 0]),
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0x1D: np.array([1, 0, 1, 1, 1, 0, 0, 0, 0]),
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0x1E: np.array([0, 1, 1, 1, 1, 0, 0, 0, 0]),
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0x1F: np.array([1, 1, 1, 1, 1, 0, 0, 0, 0]),
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}
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deck = []
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for i in range(3, 15):
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@ -63,7 +103,7 @@ class Env:
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Doudizhu multi-agent wrapper
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"""
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def __init__(self, objective, old_model, legacy_model=False, lite_model = False):
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def __init__(self, objective, old_model, legacy_model=False, lite_model = False, unified_model = False):
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"""
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Objective is wp/adp/logadp. It indicates whether considers
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bomb in reward calculation. Here, we use dummy agents.
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@ -77,7 +117,8 @@ class Env:
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"""
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self.objective = objective
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self.use_legacy = legacy_model
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self.use_general = not old_model
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self.use_unified = unified_model
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self.use_general = not old_model and not unified_model
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self.lite_model = lite_model
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# Initialize players
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@ -107,6 +148,8 @@ class Env:
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'landlord_up': _deck[33:58],
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'landlord_front': _deck[58:83],
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'landlord_down': _deck[83:108],
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'three_landlord_cards': _deck[25:33],
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'three_landlord_cards_all': _deck[25:33],
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}
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for key in card_play_data:
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card_play_data[key].sort()
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@ -124,7 +167,7 @@ class Env:
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self._env.info_sets[pos].player_id = pid
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self.infoset = self._game_infoset
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return get_obs(self.infoset, self.use_general, self.use_legacy, self.lite_model)
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return get_obs(self.infoset, self.use_general, self.use_legacy, self.lite_model, self.use_unified)
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def step(self, action):
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"""
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@ -250,7 +293,7 @@ class DummyAgent(object):
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self.action = action
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def get_obs(infoset, use_general=True, use_legacy = False, lite_model = False):
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def get_obs(infoset, use_general=True, use_legacy = False, lite_model = False, use_unified=False):
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"""
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This function obtains observations with imperfect information
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from the infoset. It has three branches since we encode
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@ -278,6 +321,8 @@ def get_obs(infoset, use_general=True, use_legacy = False, lite_model = False):
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if infoset.player_position not in ["landlord", "landlord_up", "landlord_front", "landlord_down"]:
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raise ValueError('')
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return _get_obs_general(infoset, infoset.player_position, lite_model)
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elif use_unified:
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return _get_obs_unified(infoset, infoset.player_position, lite_model)
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else:
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if infoset.player_position == 'landlord':
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return _get_obs_landlord(infoset, use_legacy, lite_model)
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@ -312,6 +357,12 @@ def _get_one_hot_array(num_left_cards, max_num_cards, compress_size = 0):
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return one_hot
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def _cards2noise(list_cards, compressed_form = False):
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if compressed_form:
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return np.random.randint(0, 2, 69, dtype=np.int8)
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else:
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return np.random.randint(0, 2, 108, dtype=np.int8)
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def _cards2array(list_cards, compressed_form = False):
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"""
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A utility function that transforms the actions, i.e.,
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@ -381,7 +432,7 @@ def _cards2array(list_cards, compressed_form = False):
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# # action_seq_array = action_seq_array.reshape(5, 162)
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# return action_seq_array
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def _action_seq_list2array(action_seq_list, new_model=True, compressed_form = False):
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def _action_seq_list2array(action_seq_list, new_model=True, compressed_form = False, use_unified = False):
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"""
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A utility function to encode the historical moves.
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We encode the historical 20 actions. If there is
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@ -404,6 +455,19 @@ def _action_seq_list2array(action_seq_list, new_model=True, compressed_form = Fa
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for row, list_cards in enumerate(action_seq_list):
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if list_cards != []:
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action_seq_array[row, :108] = _cards2array(list_cards[1], compressed_form)
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elif use_unified:
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if compressed_form:
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action_seq_array = np.zeros((len(action_seq_list), 69))
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for row, list_cards in enumerate(action_seq_list):
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if list_cards != []:
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action_seq_array[row, :] = _cards2array(list_cards[1], compressed_form)
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action_seq_array = action_seq_array.reshape(24, 276)
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else:
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action_seq_array = np.zeros((len(action_seq_list), 108))
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for row, list_cards in enumerate(action_seq_list):
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if list_cards != []:
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action_seq_array[row, :] = _cards2array(list_cards[1], compressed_form)
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action_seq_array = action_seq_array.reshape(24, 432)
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else:
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if compressed_form:
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action_seq_array = np.zeros((len(action_seq_list), 69))
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@ -442,7 +506,7 @@ def _process_action_seq(sequence, length=20, new_model=True):
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return sequence
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def _get_one_hot_bomb(bomb_num, use_legacy = False):
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def _get_one_hot_bomb(bomb_num, use_legacy = False, compressed_form = False):
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"""
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A utility function to encode the number of bombs
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into one-hot representation.
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@ -451,7 +515,7 @@ def _get_one_hot_bomb(bomb_num, use_legacy = False):
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one_hot = np.zeros(29)
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one_hot[bomb_num[0] + bomb_num[1]] = 1
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else:
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one_hot = np.zeros(56) # 14 + 15 + 27
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one_hot = np.zeros(56 if compressed_form else 95) # 14 + 15 + 27
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one_hot[bomb_num[0]] = 1
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one_hot[14 + bomb_num[1]] = 1
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one_hot[29 + bomb_num[2]] = 1
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@ -1000,3 +1064,134 @@ def _get_obs_general(infoset, position, compressed_form = False):
|
|||
'z': z.astype(np.int8),
|
||||
}
|
||||
return obs
|
||||
|
||||
'''
|
||||
face_up_level 0x01: three_landlord_cards, 0x02: landlord, 0x04: landlord_up, 0x08: landlord_front, 0x10: landlord_down
|
||||
'''
|
||||
def _get_obs_unified(infoset, position, compressed_form = True, face_up_level = 0):
|
||||
num_legal_actions = len(infoset.legal_actions)
|
||||
my_handcards = _cards2array(infoset.player_hand_cards, compressed_form)
|
||||
my_handcards_batch = np.repeat(my_handcards[np.newaxis, :],
|
||||
num_legal_actions, axis=0)
|
||||
|
||||
other_handcards = _cards2array(infoset.other_hand_cards, compressed_form)
|
||||
|
||||
my_action_batch = np.zeros(my_handcards_batch.shape)
|
||||
for j, action in enumerate(infoset.legal_actions):
|
||||
my_action_batch[j, :] = _cards2array(action, compressed_form)
|
||||
|
||||
landlord_num_cards_left = _get_one_hot_array(
|
||||
infoset.num_cards_left_dict['landlord'], 33, 15 if compressed_form else 0)
|
||||
|
||||
landlord_up_num_cards_left = _get_one_hot_array(
|
||||
infoset.num_cards_left_dict['landlord_up'], 25, 8 if compressed_form else 0)
|
||||
|
||||
landlord_front_num_cards_left = _get_one_hot_array(
|
||||
infoset.num_cards_left_dict['landlord_front'], 25, 8 if compressed_form else 0)
|
||||
|
||||
landlord_down_num_cards_left = _get_one_hot_array(
|
||||
infoset.num_cards_left_dict['landlord_down'], 25, 8 if compressed_form else 0)
|
||||
|
||||
landlord_played_cards = _cards2array(
|
||||
infoset.played_cards['landlord'], compressed_form)
|
||||
|
||||
landlord_up_played_cards = _cards2array(
|
||||
infoset.played_cards['landlord_up'], compressed_form)
|
||||
|
||||
landlord_front_played_cards = _cards2array(
|
||||
infoset.played_cards['landlord_front'], compressed_form)
|
||||
|
||||
landlord_down_played_cards = _cards2array(
|
||||
infoset.played_cards['landlord_down'], compressed_form)
|
||||
|
||||
if (face_up_level & 0x01) > 0:
|
||||
three_landlord_cards = _cards2array(
|
||||
infoset.three_landlord_cards, compressed_form)
|
||||
|
||||
three_landlord_cards_all = _cards2array(
|
||||
infoset.three_landlord_cards_all, compressed_form)
|
||||
else:
|
||||
three_landlord_cards = _cards2noise(
|
||||
infoset.three_landlord_cards, compressed_form)
|
||||
|
||||
three_landlord_cards_all = _cards2noise(
|
||||
infoset.three_landlord_cards_all, compressed_form)
|
||||
|
||||
if (face_up_level & 0x02) > 0:
|
||||
landlord_cards = _cards2array(
|
||||
infoset.all_handcards['landlord'], compressed_form)
|
||||
else:
|
||||
landlord_cards = _cards2noise(
|
||||
infoset.all_handcards['landlord'], compressed_form)
|
||||
|
||||
if (face_up_level & 0x04) > 0:
|
||||
landlord_up_cards = _cards2array(
|
||||
infoset.all_handcards['landlord_up'], compressed_form)
|
||||
else:
|
||||
landlord_up_cards = _cards2noise(
|
||||
infoset.all_handcards['landlord_up'], compressed_form)
|
||||
|
||||
if (face_up_level & 0x08) > 0:
|
||||
landlord_front_cards = _cards2array(
|
||||
infoset.all_handcards['landlord_front'], compressed_form)
|
||||
else:
|
||||
landlord_front_cards = _cards2noise(
|
||||
infoset.all_handcards['landlord_front'], compressed_form)
|
||||
|
||||
if (face_up_level & 0x10) > 0:
|
||||
landlord_down_cards = _cards2array(
|
||||
infoset.all_handcards['landlord_down'], compressed_form)
|
||||
else:
|
||||
landlord_down_cards = _cards2noise(
|
||||
infoset.all_handcards['landlord_down'], compressed_form)
|
||||
|
||||
bomb_num = _get_one_hot_bomb(
|
||||
infoset.bomb_num, compressed_form=compressed_form) # 56/95
|
||||
base_info = np.hstack((
|
||||
PositionInfoArray[position], # 4
|
||||
FaceUpLevelArray[face_up_level], # 9
|
||||
bomb_num, #56
|
||||
))
|
||||
num_cards_left = np.hstack((
|
||||
landlord_num_cards_left, # 33/18
|
||||
landlord_up_num_cards_left, # 25/17
|
||||
landlord_front_num_cards_left, # 25/17
|
||||
landlord_down_num_cards_left)) # 25/17
|
||||
|
||||
x_no_action = _action_seq_list2array(_process_action_seq(infoset.card_play_action_seq, 96), False, compressed_form, True) # 24*276 / 24*432
|
||||
|
||||
x_batch = np.repeat(
|
||||
x_no_action[np.newaxis, :],
|
||||
num_legal_actions, axis=0)
|
||||
|
||||
z =np.vstack((
|
||||
base_info, # 69
|
||||
num_cards_left, # 108 / 18+17*3=69
|
||||
my_handcards, # 108/69
|
||||
other_handcards, # 108/69
|
||||
landlord_played_cards, # 108/69
|
||||
landlord_up_played_cards, # 108/69
|
||||
landlord_front_played_cards, # 108/69
|
||||
landlord_down_played_cards, # 108/69
|
||||
landlord_cards, # 108/69
|
||||
landlord_up_cards, # 108/69
|
||||
landlord_front_cards, # 108/69
|
||||
landlord_down_cards, # 108/69
|
||||
three_landlord_cards, # 108/69
|
||||
three_landlord_cards_all, # 108/69
|
||||
))
|
||||
|
||||
_z_batch = np.repeat(
|
||||
z[np.newaxis, :, :],
|
||||
num_legal_actions, axis=0)
|
||||
my_action_batch = my_action_batch[:,np.newaxis,:]
|
||||
z_batch = np.concatenate((my_action_batch, _z_batch), axis=1)
|
||||
obs = {
|
||||
'position': position,
|
||||
'x_batch': x_batch.astype(np.float32),
|
||||
'z_batch': z_batch.astype(np.float32),
|
||||
'legal_actions': infoset.legal_actions,
|
||||
'x_no_action': x_no_action.astype(np.int8),
|
||||
'z': z.astype(np.int8),
|
||||
}
|
||||
return obs
|
||||
|
|
|
|||
|
|
@ -127,7 +127,8 @@ class GameEnv(object):
|
|||
|
||||
self.card_play_action_seq = []
|
||||
|
||||
# self.three_landlord_cards = None
|
||||
self.three_landlord_cards = None
|
||||
self.three_landlord_cards_all = None
|
||||
self.game_over = False
|
||||
|
||||
self.acting_player_position = None
|
||||
|
|
@ -185,7 +186,8 @@ class GameEnv(object):
|
|||
card_play_data['landlord_front']
|
||||
self.info_sets['landlord_down'].player_hand_cards = \
|
||||
card_play_data['landlord_down']
|
||||
# self.three_landlord_cards = card_play_data['three_landlord_cards']
|
||||
self.three_landlord_cards = card_play_data['three_landlord_cards']
|
||||
self.three_landlord_cards_all = card_play_data['three_landlord_cards']
|
||||
self.get_acting_player_position()
|
||||
self.game_infoset = self.get_infoset()
|
||||
|
||||
|
|
@ -253,15 +255,15 @@ class GameEnv(object):
|
|||
|
||||
self.played_cards[self.acting_player_position] += action
|
||||
|
||||
# if self.acting_player_position == 'landlord' and \
|
||||
# len(action) > 0 and \
|
||||
# len(self.three_landlord_cards) > 0:
|
||||
# for card in action:
|
||||
# if len(self.three_landlord_cards) > 0:
|
||||
# if card in self.three_landlord_cards:
|
||||
# self.three_landlord_cards.remove(card)
|
||||
# else:
|
||||
# break
|
||||
if self.acting_player_position == 'landlord' and \
|
||||
len(action) > 0 and \
|
||||
len(self.three_landlord_cards) > 0:
|
||||
for card in action:
|
||||
if len(self.three_landlord_cards) > 0:
|
||||
if card in self.three_landlord_cards:
|
||||
self.three_landlord_cards.remove(card)
|
||||
else:
|
||||
break
|
||||
|
||||
self.game_done()
|
||||
if not self.game_over:
|
||||
|
|
@ -333,7 +335,8 @@ class GameEnv(object):
|
|||
def reset(self):
|
||||
self.card_play_action_seq = []
|
||||
|
||||
# self.three_landlord_cards = None
|
||||
self.three_landlord_cards = None
|
||||
self.three_landlord_cards_all = None
|
||||
self.game_over = False
|
||||
|
||||
self.acting_player_position = None
|
||||
|
|
@ -397,8 +400,10 @@ class GameEnv(object):
|
|||
|
||||
self.info_sets[self.acting_player_position].played_cards = \
|
||||
self.played_cards
|
||||
# self.info_sets[self.acting_player_position].three_landlord_cards = \
|
||||
# self.three_landlord_cards
|
||||
self.info_sets[self.acting_player_position].three_landlord_cards = \
|
||||
self.three_landlord_cards
|
||||
self.info_sets[self.acting_player_position].three_landlord_cards_all = \
|
||||
self.three_landlord_cards_all
|
||||
self.info_sets[self.acting_player_position].card_play_action_seq = \
|
||||
self.card_play_action_seq
|
||||
|
||||
|
|
@ -424,7 +429,8 @@ class InfoSet(object):
|
|||
# The number of cards left for each player. It is a dict with str-->int
|
||||
self.num_cards_left_dict = None
|
||||
# The three landload cards. A list.
|
||||
# self.three_landlord_cards = None
|
||||
self.three_landlord_cards = None
|
||||
self.three_landlord_cards_all = None
|
||||
# The historical moves. It is a list of list
|
||||
self.card_play_action_seq = None
|
||||
# The union of the hand cards of the other two players for the current player
|
||||
|
|
|
|||
|
|
@ -21,7 +21,8 @@ def generate():
|
|||
'landlord_up': _deck[33:58],
|
||||
'landlord_front': _deck[58:83],
|
||||
'landlord_down': _deck[83:108],
|
||||
# 'three_landlord_cards': _deck[25:33],
|
||||
'three_landlord_cards': _deck[25:33],
|
||||
'three_landlord_cards_all': _deck[25:33],
|
||||
}
|
||||
for key in card_play_data:
|
||||
card_play_data[key].sort()
|
||||
|
|
|
|||
Loading…
Reference in New Issue