def __init__(self,seed,nS,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
"""
Network capable of processing any number of prior actions
Num Categories: nA (check,fold,call,bet,raise)
Num Betsizes: nB (various betsizes)
"""
super().__init__()
self.activation = activation
# self.seed = torch.manual_seed(seed)
self.nS = nS
self.nA = nA
self.nB = nB
self.hand_emb = Embedder(5,64)
self.action_emb = Embedder(6,63)
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA,self.nB)
self.preprocess = PreProcessHistory(params)
self.max_length = 10
self.emb = 512
n_heads = 8
depth = 2
self.positional_emb = Embedder(self.max_length,128)
self.lstm = nn.LSTM(self.emb, 256)
# self.transformer = CTransformer(self.emb,n_heads,depth,self.max_length,self.combined_output,max_pool=False)
self.mapping = params['mapping']
self.noise = GaussianNoise(is_relative_detach=True)
self.fc1 = nn.Linear(128,hidden_dims[0])
self.fc2 = nn.Linear(hidden_dims[0],hidden_dims[1])
self.fc3 = nn.Linear(2560,self.combined_output)
def __init__(self,
seed,
nO,
nA,
nB,
params,
hidden_dims=(64, 64),
activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nO = nO
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.maxlen = params['maxlen']
self.mapping = params['state_mapping']
self.device = params['device']
# self.emb = params['embedding_size']
self.helper_functions = NetworkFunctions(self.nA, self.nB)
self.process_input = PreProcessLayer(params)
self.lstm = nn.LSTM(1280, 128)
self.policy_out = nn.Linear(1280, self.combined_output)
self.noise = GaussianNoise(self.device)
emb = params['transformer_in']
n_heads = 8
depth = 2
self.transformer = CTransformer(emb, n_heads, depth, self.maxlen,
params['transformer_out'])
self.dropout = nn.Dropout(0.5)
self.value_output = nn.Linear(params['transformer_out'], 1)
self.advantage_output = nn.Linear(params['transformer_out'],
self.combined_output)
def __init__(self,
seed,
nS,
nA,
nB,
params,
hidden_dims=(64, 64),
activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nS = nS
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA, self.nB)
self.maxlen = params['maxlen']
self.process_input = PreProcessLayer(params)
# self.seed = torch.manual_seed(seed)
self.mapping = params['mapping']
self.hand_emb = Embedder(5, 64)
self.action_emb = Embedder(6, 64)
self.betsize_emb = Embedder(self.nB, 64)
self.noise = GaussianNoise()
self.emb = 1248
n_heads = 8
depth = 2
self.lstm = nn.LSTM(self.emb, 128)
# self.transformer = CTransformer(emb,n_heads,depth,self.max_length,self.nA)
self.fc1 = nn.Linear(528, hidden_dims[0])
self.fc2 = nn.Linear(hidden_dims[0], hidden_dims[1])
self.fc3 = nn.Linear(1280, self.combined_output)
self.dropout = nn.Dropout(0.5)
class FlatBetsizeActor(nn.Module):
def __init__(self,seed,nS,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
"""
Num Categories: nA (check,fold,call,bet,raise)
Num Betsizes: nB (various betsizes)
"""
super().__init__()
self.activation = activation
self.nS = nS
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA,self.nB)
self.mapping = params['mapping']
self.hand_emb = Embedder(5,64)
self.action_emb = Embedder(6,64)
self.betsize_emb = Embedder(self.nB,64)
self.noise = GaussianNoise()
self.fc1 = nn.Linear(129,hidden_dims[0])
self.fc2 = nn.Linear(hidden_dims[0],hidden_dims[1])
self.fc3 = nn.Linear(hidden_dims[1],self.combined_output)
def forward(self,state,action_mask,betsize_mask):
mask = combined_masks(action_mask,betsize_mask)
x = state
hand = x[:,self.mapping['state']['rank']].long()
last_action = x[:,self.mapping['state']['previous_action']].long()
previous_betsize = x[:,self.mapping['state']['previous_betsize']].float()
if previous_betsize.dim() == 1:
previous_betsize = previous_betsize.unsqueeze(1)
hand = self.hand_emb(hand)
last_action_emb = self.action_emb(last_action)
# print('hand,last_action_emb,previous_betsize',hand.size(),last_action_emb.size(),previous_betsize.size())
x = torch.cat([hand,last_action_emb,previous_betsize],dim=-1)
x = self.activation(self.fc1(x))
x = self.activation(self.fc2(x))
cateogry_logits = self.fc3(x)
cateogry_logits = self.noise(cateogry_logits)
action_soft = F.softmax(cateogry_logits,dim=-1)
# print(action_soft.size(),mask.size())
action_probs = norm_frequencies(action_soft,mask)
# action_probs = action_probs * mask
# action_probs /= torch.sum(action_probs)
m = Categorical(action_probs)
action = m.sample()
action_category,betsize_category = self.helper_functions.unwrap_action(action,last_action)
# print('state',state)
# print('action_category,betsize_category',action_category,betsize_category)
outputs = {
'action':action,
'action_category':action_category,
'action_prob':m.log_prob(action),
'action_probs':action_probs,
'betsize':betsize_category
}
return outputs
def __init__(self,seed,nS,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
"""
Num Categories: nA (check,fold,call,bet,raise)
Num Betsizes: nB (various betsizes)
"""
super().__init__()
self.activation = activation
self.nS = nS
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA,self.nB)
self.mapping = params['mapping']
self.hand_emb = Embedder(5,64)
self.action_emb = Embedder(6,64)
self.betsize_emb = Embedder(self.nB,64)
self.noise = GaussianNoise()
self.fc1 = nn.Linear(129,hidden_dims[0])
self.fc2 = nn.Linear(hidden_dims[0],hidden_dims[1])
self.fc3 = nn.Linear(hidden_dims[1],self.combined_output)
def __init__(self,
seed,
nS,
nA,
nB,
params,
hidden_dims=(64, 64),
activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nS = nS
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA, self.nB)
self.maxlen = params['maxlen']
self.device = params['device']
self.epsilon = params['epsilon']
self.epsilon_weights = params['epsilon_weights'].to(self.device)
self.process_input = PreProcessLayer(params)
# self.seed = torch.manual_seed(seed)
self.state_mapping = params['state_mapping']
self.action_emb = Embedder(Action.UNOPENED, 64)
self.betsize_emb = Embedder(self.nB, 64)
self.noise = GaussianNoise(self.device)
self.emb = 1248
n_heads = 8
depth = 2
# self.attention = EncoderAttention(params['lstm_in'],params['lstm_out'])
self.lstm = nn.LSTM(params['lstm_in'],
params['lstm_out'],
bidirectional=True)
self.batchnorm = nn.BatchNorm1d(self.maxlen)
# self.blocks = nn.Sequential(
# IdentityBlock(hidden_dims=(2560,2560,512),activation=F.leaky_relu),
# IdentityBlock(hidden_dims=(512,512,256),activation=F.leaky_relu),
# )
self.fc_final = nn.Linear(5120, self.combined_output)
class CombinedNet(Network):
def __init__(self,
seed,
nO,
nA,
nB,
params,
hidden_dims=(64, 64),
activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nO = nO
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.maxlen = params['maxlen']
self.mapping = params['state_mapping']
self.device = params['device']
# self.emb = params['embedding_size']
self.helper_functions = NetworkFunctions(self.nA, self.nB)
self.process_input = PreProcessLayer(params)
self.lstm = nn.LSTM(1280, 128)
self.policy_out = nn.Linear(1280, self.combined_output)
self.noise = GaussianNoise(self.device)
emb = params['transformer_in']
n_heads = 8
depth = 2
self.transformer = CTransformer(emb, n_heads, depth, self.maxlen,
params['transformer_out'])
self.dropout = nn.Dropout(0.5)
self.value_output = nn.Linear(params['transformer_out'], 1)
self.advantage_output = nn.Linear(params['transformer_out'],
self.combined_output)
def forward(self, state, action_mask, betsize_mask):
x = torch.tensor(state, dtype=torch.float32).to(self.device)
action_mask = torch.tensor(action_mask,
dtype=torch.float).to(self.device)
betsize_mask = torch.tensor(betsize_mask,
dtype=torch.float).to(self.device)
mask = combined_masks(action_mask, betsize_mask)
out = self.process_input(x)
# Actor
B, M, c = out.size()
n_padding = self.maxlen - M
if n_padding < 0:
h = out[:, -self.maxlen:, :]
else:
padding = torch.zeros(B, n_padding, out.size(-1)).to(self.device)
h = torch.cat((out, padding), dim=1)
lstm_out, _ = self.lstm(h)
t_logits = self.policy_out(lstm_out.view(-1))
category_logits = self.noise(t_logits)
action_soft = F.softmax(category_logits, dim=-1)
action_probs = norm_frequencies(action_soft, mask)
m = Categorical(action_probs)
action = m.sample()
action_category, betsize_category = self.helper_functions.unwrap_action(
action, state[:, -1, self.mapping['last_action']])
outputs = {
'action': action.item(),
'action_category': action_category.item(),
'action_prob': m.log_prob(action),
'action_probs': action_probs,
'betsize': betsize_category.item()
}
# Critic
q_input = self.transformer(out)
a = self.advantage_output(q_input)
v = self.value_output(q_input)
v = v.expand_as(a)
q = v + a - a.mean(-1, keepdim=True).expand_as(a)
outputs['value'] = q.squeeze(0)
return outputs
class HoldemBaseline(Network):
def __init__(self,
seed,
nS,
nA,
nB,
params,
hidden_dims=(64, 64),
activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nS = nS
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA, self.nB)
self.maxlen = params['maxlen']
self.process_input = PreProcessLayer(params)
# self.seed = torch.manual_seed(seed)
self.mapping = params['mapping']
self.hand_emb = Embedder(5, 64)
self.action_emb = Embedder(6, 64)
self.betsize_emb = Embedder(self.nB, 64)
self.noise = GaussianNoise()
self.emb = 1248
n_heads = 8
depth = 2
self.lstm = nn.LSTM(self.emb, 128)
# self.transformer = CTransformer(emb,n_heads,depth,self.max_length,self.nA)
self.fc1 = nn.Linear(528, hidden_dims[0])
self.fc2 = nn.Linear(hidden_dims[0], hidden_dims[1])
self.fc3 = nn.Linear(1280, self.combined_output)
self.dropout = nn.Dropout(0.5)
def forward(self, state, action_mask, betsize_mask):
mask = combined_masks(action_mask, betsize_mask)
x = state
if x.dim() == 2:
x = x.unsqueeze(0)
out = self.process_input(x).unsqueeze(0)
B, M, c = out.size()
n_padding = max(self.maxlen - M, 0)
padding = torch.zeros(B, n_padding, out.size(-1))
h = torch.cat((out, padding), dim=1)
lstm_out, _ = self.lstm(h)
t_logits = self.fc3(lstm_out.view(-1))
category_logits = self.noise(t_logits)
action_soft = F.softmax(category_logits, dim=-1)
action_probs = norm_frequencies(action_soft, mask)
m = Categorical(action_probs)
action = m.sample()
action_category, betsize_category = self.helper_functions.unwrap_action(
action, state[:, -1, self.mapping['state']['previous_action']])
outputs = {
'action': action,
'action_category': action_category,
'action_prob': m.log_prob(action),
'action_probs': action_probs,
'betsize': betsize_category
}
return outputs
class OmahaActor(Network):
def __init__(self,
seed,
nS,
nA,
nB,
params,
hidden_dims=(64, 64),
activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nS = nS
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA, self.nB)
self.maxlen = params['maxlen']
self.device = params['device']
self.epsilon = params['epsilon']
self.epsilon_weights = params['epsilon_weights'].to(self.device)
self.process_input = PreProcessLayer(params)
# self.seed = torch.manual_seed(seed)
self.state_mapping = params['state_mapping']
self.action_emb = Embedder(Action.UNOPENED, 64)
self.betsize_emb = Embedder(self.nB, 64)
self.noise = GaussianNoise(self.device)
self.emb = 1248
n_heads = 8
depth = 2
# self.attention = EncoderAttention(params['lstm_in'],params['lstm_out'])
self.lstm = nn.LSTM(params['lstm_in'],
params['lstm_out'],
bidirectional=True)
self.batchnorm = nn.BatchNorm1d(self.maxlen)
# self.blocks = nn.Sequential(
# IdentityBlock(hidden_dims=(2560,2560,512),activation=F.leaky_relu),
# IdentityBlock(hidden_dims=(512,512,256),activation=F.leaky_relu),
# )
self.fc_final = nn.Linear(5120, self.combined_output)
def set_device(self, device):
self.device = device
self.process_input.set_device(device)
def forward(self, state, action_mask, betsize_mask, target=False):
"""
state: B,M,39
"""
if not isinstance(state, torch.Tensor):
state = torch.tensor(state, dtype=torch.float32).to(self.device)
action_mask = torch.tensor(action_mask,
dtype=torch.float32).to(self.device)
betsize_mask = torch.tensor(betsize_mask,
dtype=torch.float32).to(self.device)
mask = combined_masks(action_mask, betsize_mask)
if target and np.random.random() < self.epsilon:
B = state.size(0)
# pick random legal move
action_masked = self.epsilon_weights * mask
action_probs = action_masked / action_masked.sum(-1).unsqueeze(-1)
action = action_probs.multinomial(num_samples=1, replacement=False)
action_prob = torch.zeros(B, 1)
else:
out = self.process_input(state)
B, M, c = state.size()
n_padding = self.maxlen - M
if n_padding < 0:
h = out[:, -self.maxlen:, :]
else:
padding = torch.zeros(B, n_padding,
out.size(-1)).to(self.device)
h = torch.cat((padding, out), dim=1)
lstm_out, hidden_states = self.lstm(h)
norm = self.batchnorm(lstm_out)
# self.attention(out)
# blocks_out = self.blocks(lstm_out.view(-1))
t_logits = self.fc_final(norm.view(B, -1))
category_logits = self.noise(t_logits)
# skip connection
# category_logits += h
action_soft = F.softmax(category_logits, dim=-1)
action_probs = norm_frequencies(action_soft, mask)
m = Categorical(action_probs)
action = m.sample()
action_prob = m.log_prob(action)
previous_action = torch.as_tensor(
state[:, -1, self.state_mapping['last_action']]).to(self.device)
action_category, betsize_category = self.helper_functions.batch_unwrap_action(
action, previous_action)
if B > 1:
# batch training
outputs = {
'action': action,
'action_category': action_category,
'action_prob': action_prob,
'action_probs': action_probs,
'betsize': betsize_category
}
else:
# playing hand
outputs = {
'action': action.item(),
'action_category': action_category.item(),
'action_prob': action_prob,
'action_probs': action_probs,
'betsize': betsize_category.item()
}
return outputs
class OmahaBatchActor(Network):
def __init__(self,
seed,
nS,
nA,
nB,
params,
hidden_dims=(64, 64),
activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nS = nS
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA, self.nB)
self.maxlen = params['maxlen']
self.device = params['device']
self.process_input = PreProcessLayer(params)
# self.seed = torch.manual_seed(seed)
self.state_mapping = params['state_mapping']
self.hand_emb = Embedder(5, 64)
self.action_emb = Embedder(Action.UNOPENED, 64)
self.betsize_emb = Embedder(self.nB, 64)
self.noise = GaussianNoise(self.device)
self.emb = 1248
n_heads = 8
depth = 2
self.lstm = nn.LSTM(1280, 128, bidirectional=True)
self.batchnorm = nn.BatchNorm1d(self.maxlen)
# self.blocks = nn.Sequential(
# IdentityBlock(hidden_dims=(2560,2560,512),activation=F.leaky_relu),
# IdentityBlock(hidden_dims=(512,512,256),activation=F.leaky_relu),
# )
self.fc_final = nn.Linear(2560, self.combined_output)
self.dropout = nn.Dropout(0.5)
def forward(self, state, action_mask, betsize_mask):
x = torch.tensor(state, dtype=torch.float32).to(self.device)
action_mask = torch.tensor(action_mask,
dtype=torch.float).to(self.device)
betsize_mask = torch.tensor(betsize_mask,
dtype=torch.float).to(self.device)
mask = combined_masks(action_mask, betsize_mask)
out = self.process_input(x)
B, M, c = out.size()
n_padding = self.maxlen - M
if n_padding < 0:
h = out[:, -self.maxlen:, :]
else:
padding = torch.zeros(B, n_padding, out.size(-1)).to(self.device)
h = torch.cat((out, padding), dim=1)
lstm_out, _ = self.lstm(h)
norm = self.batchnorm(lstm_out)
# blocks_out = self.blocks(lstm_out.view(-1))
t_logits = self.fc_final(norm.view(-1))
category_logits = self.noise(t_logits)
action_soft = F.softmax(category_logits, dim=-1)
action_probs = norm_frequencies(action_soft, mask)
m = Categorical(action_probs)
action = m.sample()
action_category, betsize_category = self.helper_functions.unwrap_action(
action, state[:, -1, self.state_mapping['last_action']])
outputs = {
'action': action.item(),
'action_category': action_category.item(),
'action_prob': m.log_prob(action),
'action_probs': action_probs,
'betsize': betsize_category.item()
}
return outputs
class FlatHistoricalActor(nn.Module):
def __init__(self,seed,nS,nA,nB,params,hidden_dims=(64,64),activation=F.leaky_relu):
"""
Network capable of processing any number of prior actions
Num Categories: nA (check,fold,call,bet,raise)
Num Betsizes: nB (various betsizes)
"""
super().__init__()
self.activation = activation
# self.seed = torch.manual_seed(seed)
self.nS = nS
self.nA = nA
self.nB = nB
self.hand_emb = Embedder(5,64)
self.action_emb = Embedder(6,63)
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA,self.nB)
self.preprocess = PreProcessHistory(params)
self.max_length = 10
self.emb = 512
n_heads = 8
depth = 2
self.positional_emb = Embedder(self.max_length,128)
self.lstm = nn.LSTM(self.emb, 256)
# self.transformer = CTransformer(self.emb,n_heads,depth,self.max_length,self.combined_output,max_pool=False)
self.mapping = params['mapping']
self.noise = GaussianNoise(is_relative_detach=True)
self.fc1 = nn.Linear(128,hidden_dims[0])
self.fc2 = nn.Linear(hidden_dims[0],hidden_dims[1])
self.fc3 = nn.Linear(2560,self.combined_output)
def forward(self,state,action_mask,betsize_mask):
mask = combined_masks(action_mask,betsize_mask)
if mask.dim() > 1:
mask = mask[-1]
x = state
if x.dim() == 2:
x = x.unsqueeze(0)
out = self.preprocess(x)
M,C = out.size()
n_padding = self.max_length - M
padding = torch.zeros(n_padding,out.size(-1))
h = torch.cat((out,padding),dim=0).unsqueeze(0)
# pos_emd = self.positional_emb(torch.arange(self.max_length))
# padding_mask_o = torch.ones(M,self.emb)
# padding_mask_z = torch.zeros(n_padding,self.emb)
# padding_mask = torch.cat((padding_mask_o,padding_mask_z),dim=0)
# pos_emd = (pos_emd.view(-1) * padding_mask.view(-1)).view(h.size(0),self.emb)
# h = h + pos_emd
# x = (h + pos_emd).unsqueeze(0)
# x = self.activation(self.fc1(h))
# x = self.activation(self.fc2(x)).view(-1)
# t_logits = self.fc3(x).unsqueeze(0)
x,_ = self.lstm(h)
# x_stripped = (x.view(-1) * padding_mask.view(-1)).view(1,-1)
t_logits = self.fc3(x.view(-1))
# t_logits = self.transformer(x)
cateogry_logits = self.noise(t_logits)
# distribution_inputs = F.log_softmax(cateogry_logits, dim=1) * mask
action_soft = F.softmax(cateogry_logits,dim=-1)
action_probs = norm_frequencies(action_soft,mask)
last_action = state[M-1,self.mapping['state']['previous_action']].long().unsqueeze(-1)
m = Categorical(action_probs)
action = m.sample()
action_category,betsize_category = self.helper_functions.unwrap_action(action,last_action)
outputs = {
'action':action,
'action_category':action_category,
'action_prob':m.log_prob(action),
'action_probs':m.probs,
'betsize':betsize_category
}
return outputs
class FlatAC(nn.Module):
def __init__(self,seed,nS,nA,nB,params,hidden_dims=(256,128),activation=F.leaky_relu):
"""
Network capable of processing any number of prior actions
Num Categories: nA (check,fold,call,bet,raise)
Num Betsizes: nB (various betsizes)
"""
super().__init__()
self.activation = activation
self.nS = nS
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA,self.nB)
self.preprocess = PreProcessHistory(params)
self.max_length = 10
emb = 128
n_heads = 8
depth = 2
self.positional_emb = Embedder(self.max_length,128)
self.transformer = CTransformer(emb,n_heads,depth,self.max_length,self.combined_output,max_pool=False)
self.seed = torch.manual_seed(seed)
self.mapping = params['mapping']
self.noise = GaussianNoise(is_relative_detach=True)
self.fc1 = nn.Linear(513,hidden_dims[0])
self.fc2 = nn.Linear(hidden_dims[0],hidden_dims[1])
self.fc3 = nn.Linear(1280,self.combined_output)
self.value_output = nn.Linear(64,1)
self.advantage_output = nn.Linear(64,self.combined_output)
def forward(self,state,action_mask,betsize_mask):
# last_state = state[-1].unsqueeze(0)
mask = combined_masks(action_mask,betsize_mask)
if mask.dim() > 1:
mask = mask[-1]
x = state
M,C = x.size()
out = self.preprocess(x)
x = self.activation(self.fc1(out))
x = self.activation(self.fc2(x))
n_padding = self.max_length - M
padding = torch.zeros(n_padding,out.size(-1))
h = torch.cat((out,padding),dim=0)
pos_emd = self.positional_emb(torch.arange(self.max_length))
h = h + pos_emd
# x = (h + pos_emd).unsqueeze(0)
t_logits = self.fc3(h.view(-1)).unsqueeze(0)
# t_logits = self.transformer(x)
cateogry_logits = self.noise(t_logits)
# distribution_inputs = F.log_softmax(cateogry_logits, dim=1) * mask
action_soft = F.softmax(cateogry_logits,dim=-1)
action_probs = norm_frequencies(action_soft,mask)
last_action = state[-1,self.mapping['state']['previous_action']].long().unsqueeze(-1)
m = Categorical(action_probs)
action = m.sample()
action_category,betsize_category = self.helper_functions.unwrap_action(action,last_action)
q_input = x.view(M,-1)
a = self.advantage_output(q_input)
v = self.value_output(q_input)
v = v.expand_as(a)
q = v + a - a.mean(1,keepdim=True).expand_as(a)
outputs = {
'action':action,
'action_category':action_category,
'action_prob':m.log_prob(action),
'action_probs':m.probs,
'betsize':betsize_category,
'value':q
}
return outputs
class OmahaActor(Network):
def __init__(self,
seed,
nS,
nA,
nB,
params,
hidden_dims=(64, 64),
activation=F.leaky_relu):
super().__init__()
self.activation = activation
self.nS = nS
self.nA = nA
self.nB = nB
self.combined_output = nA - 2 + nB
self.helper_functions = NetworkFunctions(self.nA, self.nB)
self.maxlen = params['maxlen']
self.device = params['device']
self.process_input = PreProcessLayer(params)
# self.seed = torch.manual_seed(seed)
self.state_mapping = params['state_mapping']
self.action_emb = Embedder(Action.UNOPENED, 64)
self.betsize_emb = Embedder(self.nB, 64)
self.noise = GaussianNoise(self.device)
self.emb = 1248
n_heads = 8
depth = 2
# self.attention = EncoderAttention(params['lstm_in'],params['lstm_out'])
self.lstm = nn.LSTM(params['lstm_in'],
params['lstm_out'],
bidirectional=True)
self.batchnorm = nn.BatchNorm1d(self.maxlen)
# self.blocks = nn.Sequential(
# IdentityBlock(hidden_dims=(2560,2560,512),activation=F.leaky_relu),
# IdentityBlock(hidden_dims=(512,512,256),activation=F.leaky_relu),
# )
self.fc_final = nn.Linear(2560, self.combined_output)
self.dropout = nn.Dropout(0.5)
def forward(self, state, action_mask, betsize_mask):
"""
state: B,M,39
"""
x = state
if not isinstance(x, torch.Tensor):
x = torch.tensor(x, dtype=torch.float32).to(self.device)
action_mask = torch.tensor(action_mask,
dtype=torch.float).to(self.device)
betsize_mask = torch.tensor(betsize_mask,
dtype=torch.float).to(self.device)
mask = combined_masks(action_mask, betsize_mask)
out = self.process_input(x)
B, M, c = out.size()
n_padding = self.maxlen - M
if n_padding < 0:
h = out[:, -self.maxlen:, :]
else:
padding = torch.zeros(B, n_padding, out.size(-1)).to(self.device)
h = torch.cat((padding, out), dim=1)
lstm_out, hidden_states = self.lstm(h)
norm = self.batchnorm(lstm_out)
# self.attention(out)
# blocks_out = self.blocks(lstm_out.view(-1))
t_logits = self.fc_final(norm.view(B, -1))
category_logits = self.noise(t_logits)
# skip connection
# category_logits += h
action_soft = F.softmax(category_logits, dim=-1)
# if torch.cuda.is_available():
# action_probs = norm_frequencies(action_soft,mask.cuda())
# previous_action = torch.as_tensor(state[:,-1,self.state_mapping['last_action']]).cuda()#.to(self.device)
# else:
action_probs = norm_frequencies(action_soft, mask)
previous_action = torch.as_tensor(
state[:, -1, self.state_mapping['last_action']]).to(self.device)
m = Categorical(action_probs)
action = m.sample()
action_category, betsize_category = self.helper_functions.batch_unwrap_action(
action, previous_action)
if B > 1:
# batch training
outputs = {
'action': action,
'action_category': action_category,
'action_prob': m.log_prob(action),
'action_probs': action_probs,
'betsize': betsize_category
}
else:
# playing hand
outputs = {
'action': action.item(),
'action_category': action_category.item(),
'action_prob': m.log_prob(action),
'action_probs': action_probs,
'betsize': betsize_category.item()
}
return outputs