142 lines
5.1 KiB
Python
142 lines
5.1 KiB
Python
import torch
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import numpy as np
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from torch import nn
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from tianshou.data import to_torch
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class Actor(nn.Module):
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def __init__(self, layer_num, state_shape, action_shape,
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max_action, device='cpu'):
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super().__init__()
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self.device = device
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self.model = [
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nn.Linear(np.prod(state_shape), 128),
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nn.ReLU(inplace=True)]
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for i in range(layer_num):
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self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
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self.model += [nn.Linear(128, np.prod(action_shape))]
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self.model = nn.Sequential(*self.model)
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self._max = max_action
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def forward(self, s, **kwargs):
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s = to_torch(s, device=self.device, dtype=torch.float)
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batch = s.shape[0]
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s = s.view(batch, -1)
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logits = self.model(s)
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logits = self._max * torch.tanh(logits)
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return logits, None
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class ActorProb(nn.Module):
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def __init__(self, layer_num, state_shape, action_shape,
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max_action, device='cpu'):
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super().__init__()
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self.device = device
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self.model = [
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nn.Linear(np.prod(state_shape), 128),
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nn.ReLU(inplace=True)]
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for i in range(layer_num):
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self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
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self.model = nn.Sequential(*self.model)
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self.mu = nn.Linear(128, np.prod(action_shape))
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self.sigma = nn.Parameter(torch.zeros(np.prod(action_shape), 1))
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# self.sigma = nn.Linear(128, np.prod(action_shape))
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self._max = max_action
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def forward(self, s, **kwargs):
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s = to_torch(s, device=self.device, dtype=torch.float)
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batch = s.shape[0]
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s = s.view(batch, -1)
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logits = self.model(s)
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mu = self.mu(logits)
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shape = [1] * len(mu.shape)
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shape[1] = -1
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sigma = (self.sigma.view(shape) + torch.zeros_like(mu)).exp()
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# assert sigma.shape == mu.shape
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# mu = self._max * torch.tanh(self.mu(logits))
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# sigma = torch.exp(self.sigma(logits))
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return (mu, sigma), None
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class Critic(nn.Module):
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def __init__(self, layer_num, state_shape, action_shape=0, device='cpu'):
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super().__init__()
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self.device = device
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self.model = [
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nn.Linear(np.prod(state_shape) + np.prod(action_shape), 128),
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nn.ReLU(inplace=True)]
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for i in range(layer_num):
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self.model += [nn.Linear(128, 128), nn.ReLU(inplace=True)]
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self.model += [nn.Linear(128, 1)]
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self.model = nn.Sequential(*self.model)
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def forward(self, s, a=None, **kwargs):
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s = to_torch(s, device=self.device, dtype=torch.float)
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batch = s.shape[0]
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s = s.view(batch, -1)
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if a is not None:
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if not isinstance(a, torch.Tensor):
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a = torch.tensor(a, device=self.device, dtype=torch.float)
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a = a.view(batch, -1)
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s = torch.cat([s, a], dim=1)
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logits = self.model(s)
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return logits
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class RecurrentActorProb(nn.Module):
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def __init__(self, layer_num, state_shape, action_shape,
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max_action, device='cpu'):
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super().__init__()
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self.device = device
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self.nn = nn.LSTM(input_size=np.prod(state_shape), hidden_size=128,
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num_layers=layer_num, batch_first=True)
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self.mu = nn.Linear(128, np.prod(action_shape))
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self.sigma = nn.Parameter(torch.zeros(np.prod(action_shape), 1))
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def forward(self, s, **kwargs):
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s = to_torch(s, device=self.device, dtype=torch.float)
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# s [bsz, len, dim] (training) or [bsz, dim] (evaluation)
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# In short, the tensor's shape in training phase is longer than which
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# in evaluation phase.
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if len(s.shape) == 2:
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bsz, dim = s.shape
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length = 1
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else:
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bsz, length, dim = s.shape
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s = s.view(bsz, length, -1)
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logits, _ = self.nn(s)
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logits = logits[:, -1]
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mu = self.mu(logits)
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shape = [1] * len(mu.shape)
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shape[1] = -1
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sigma = (self.sigma.view(shape) + torch.zeros_like(mu)).exp()
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return (mu, sigma), None
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class RecurrentCritic(nn.Module):
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def __init__(self, layer_num, state_shape, action_shape=0, device='cpu'):
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super().__init__()
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self.state_shape = state_shape
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self.action_shape = action_shape
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self.device = device
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self.nn = nn.LSTM(input_size=np.prod(state_shape), hidden_size=128,
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num_layers=layer_num, batch_first=True)
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self.fc2 = nn.Linear(128 + np.prod(action_shape), 1)
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def forward(self, s, a=None):
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s = to_torch(s, device=self.device, dtype=torch.float)
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# s [bsz, len, dim] (training) or [bsz, dim] (evaluation)
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# In short, the tensor's shape in training phase is longer than which
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# in evaluation phase.
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assert len(s.shape) == 3
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self.nn.flatten_parameters()
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s, (h, c) = self.nn(s)
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s = s[:, -1]
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if a is not None:
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if not isinstance(a, torch.Tensor):
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a = torch.tensor(a, device=self.device, dtype=torch.float)
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s = torch.cat([s, a], dim=1)
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s = self.fc2(s)
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return s
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