mirror of
https://github.com/wassname/DeepRL.git
synced 2026-08-21 11:09:46 +08:00
Mike Clark
212ff0bc70
I think you intended this to be outside the softplus. The reason is that it should be applied just before the log to avoid `log(0)=inf`.e.g. - `log(softplus(-1000+1e-5))=log(0)=inf`. - `log(softplus(-1000)+1e-5)=log(1e-5)!=inf`. Also this fixes a NaN I had.
212 lines
7.0 KiB
Python
212 lines
7.0 KiB
Python
#######################################################################
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# Copyright (C) 2017 Shangtong Zhang(zhangshangtong.cpp@gmail.com) #
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# Permission given to modify the code as long as you keep this #
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# declaration at the top #
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#######################################################################
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from .base_network import *
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class DeterministicActorNet(nn.Module, BasicNet):
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def __init__(self,
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state_dim,
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action_dim,
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action_gate,
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action_scale,
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gpu=False,
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batch_norm=False,
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non_linear=F.relu):
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super(DeterministicActorNet, self).__init__()
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hidden_size = 64
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self.layer1 = nn.Linear(state_dim, hidden_size)
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self.layer3 = nn.Linear(hidden_size, action_dim)
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self.action_gate = action_gate
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self.action_scale = action_scale
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self.non_linear = non_linear
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if batch_norm:
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self.bn1 = nn.BatchNorm1d(hidden_size)
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self.bn2 = nn.BatchNorm1d(hidden_size)
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self.layer2 = nn.Linear(hidden_size, hidden_size)
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self.batch_norm = batch_norm
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BasicNet.__init__(self, None, gpu, False)
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self.init_weights()
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def init_weights(self):
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bound = 3e-3
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self.layer3.weight.data.uniform_(-bound, bound)
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self.layer3.bias.data.fill_(0)
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def fanin(size):
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v = 1.0 / np.sqrt(size[1])
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return torch.FloatTensor(size).uniform_(-v, v)
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self.layer1.weight.data = fanin(self.layer1.weight.data.size())
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self.layer1.bias.data.fill_(0)
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self.layer2.weight.data = fanin(self.layer2.weight.data.size())
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self.layer2.bias.data.fill_(0)
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def forward(self, x):
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x = self.to_torch_variable(x)
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x = self.non_linear(self.layer1(x))
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if self.batch_norm:
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x = self.bn1(x)
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x = self.non_linear(self.layer2(x))
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if self.batch_norm:
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x = self.bn2(x)
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x = self.layer3(x)
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x = self.action_scale * self.action_gate(x)
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return x
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def predict(self, x, to_numpy=True):
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y = self.forward(x)
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if to_numpy:
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y = y.cpu().data.numpy()
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return y
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class DeterministicCriticNet(nn.Module, BasicNet):
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def __init__(self,
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state_dim,
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action_dim,
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gpu=False,
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batch_norm=False,
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non_linear=F.relu):
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super(DeterministicCriticNet, self).__init__()
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hidden_size = 64
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self.layer1 = nn.Linear(state_dim, hidden_size)
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self.layer2 = nn.Linear(hidden_size + action_dim, hidden_size)
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self.layer3 = nn.Linear(hidden_size, 1)
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self.non_linear = non_linear
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if batch_norm:
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self.bn1 = nn.BatchNorm1d(hidden_size)
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self.bn2 = nn.BatchNorm1d(hidden_size)
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self.batch_norm = batch_norm
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BasicNet.__init__(self, None, gpu, False)
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self.init_weights()
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def init_weights(self):
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bound = 3e-3
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self.layer3.weight.data.uniform_(-bound, bound)
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self.layer3.bias.data.fill_(0)
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def fanin(size):
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v = 1.0 / np.sqrt(size[1])
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return torch.FloatTensor(size).uniform_(-v, v)
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self.layer1.weight.data = fanin(self.layer1.weight.data.size())
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self.layer1.bias.data.fill_(0)
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self.layer2.weight.data = fanin(self.layer2.weight.data.size())
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self.layer2.bias.data.fill_(0)
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def forward(self, x, action):
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x = self.to_torch_variable(x)
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action = self.to_torch_variable(action)
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x = self.non_linear(self.layer1(x))
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if self.batch_norm:
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x = self.bn1(x)
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x = self.non_linear(self.layer2(torch.cat([x, action], dim=1)))
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if self.batch_norm:
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x = self.bn2(x)
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x = self.layer3(x)
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return x
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def predict(self, x, action):
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return self.forward(x, action)
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class GaussianActorNet(nn.Module, BasicNet):
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def __init__(self, state_dim, action_dim, action_scale=1.0, action_gate=None, gpu=False, unit_std=True):
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super(GaussianActorNet, self).__init__()
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hidden_size = 64
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self.fc1 = nn.Linear(state_dim, hidden_size)
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self.fc2 = nn.Linear(hidden_size, hidden_size)
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self.action_mean = nn.Linear(hidden_size, action_dim)
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if unit_std:
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self.action_log_std = nn.Parameter(torch.zeros(1, action_dim))
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else:
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self.action_std = nn.Linear(hidden_size, action_dim)
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self.unit_std = unit_std
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self.action_scale = action_scale
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self.action_gate = action_gate
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BasicNet.__init__(self, None, gpu, False)
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def forward(self, x):
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x = self.to_torch_variable(x)
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phi = F.tanh(self.fc1(x))
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phi = F.tanh(self.fc2(phi))
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mean = self.action_mean(phi)
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if self.action_gate is not None:
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mean = self.action_scale * self.action_gate(mean)
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if self.unit_std:
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log_std = self.action_log_std.expand_as(mean)
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std = log_std.exp()
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else:
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std = F.softplus(self.action_std(phi)) + 1e-5
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log_std = std.log()
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return mean, std, log_std
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def predict(self, x):
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return self.forward(x)
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def log_density(self, x, mean, log_std, std):
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var = std.pow(2)
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log_density = -(x - mean).pow(2) / (2 * var) - 0.5 * torch.log(2 * Variable(torch.FloatTensor([np.pi])).expand_as(x)) - log_std
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return log_density.sum(1)
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def entropy(self, std):
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return 0.5 * (1 + (2 * std.pow(2) * np.pi + 1e-5).log()).sum(1).mean()
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class GaussianCriticNet(nn.Module, BasicNet):
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def __init__(self, state_dim, gpu=False):
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super(GaussianCriticNet, self).__init__()
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hidden_size = 64
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self.fc1 = nn.Linear(state_dim, hidden_size)
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self.fc2 = nn.Linear(hidden_size, hidden_size)
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self.fc_value = nn.Linear(hidden_size, 1)
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BasicNet.__init__(self, None, gpu, False)
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def forward(self, x):
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x = self.to_torch_variable(x)
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phi = F.tanh(self.fc1(x))
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phi = F.tanh(self.fc2(phi))
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value = self.fc_value(phi)
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return value
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def predict(self, x):
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return self.forward(x)
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class DisjointActorCriticNet:
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def __init__(self, actor_network_fn, critic_network_fn):
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self.actor = actor_network_fn()
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self.critic = critic_network_fn()
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def state_dict(self):
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return [self.actor.state_dict(), self.critic.state_dict()]
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def load_state_dict(self, state_dicts):
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self.actor.load_state_dict(state_dicts[0])
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self.critic.load_state_dict(state_dicts[1])
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def share_memory(self):
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self.actor.share_memory()
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self.critic.share_memory()
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def parameters(self):
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return list(self.actor.parameters()) + list(self.critic.parameters())
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def zero_grad(self):
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self.actor.zero_grad()
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self.critic.zero_grad()
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def train(self):
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self.actor.train()
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self.critic.train()
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def eval(self):
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self.actor.eval()
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self.critic.eval()
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