bd9c3c7f8d
* pre * update docs * update docs * $ in bash * size -> hidden_layer_size * doctest * doctest again * filter a warning * fix bug * fix examples * test fail * test succ
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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"""For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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"""
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def __init__(self, preprocess_net, action_shape,
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max_action, device='cpu', hidden_layer_size=128):
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super().__init__()
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self.preprocess = preprocess_net
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self.last = nn.Linear(hidden_layer_size, np.prod(action_shape))
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self._max = max_action
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def forward(self, s, state=None, info={}):
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"""s -> logits -> action"""
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logits, h = self.preprocess(s, state)
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logits = self._max * torch.tanh(self.last(logits))
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return logits, h
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class Critic(nn.Module):
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"""For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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"""
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def __init__(self, preprocess_net, device='cpu', hidden_layer_size=128):
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super().__init__()
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self.device = device
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self.preprocess = preprocess_net
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self.last = nn.Linear(hidden_layer_size, 1)
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def forward(self, s, a=None, **kwargs):
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"""(s, a) -> logits -> Q(s, a)"""
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s = to_torch(s, device=self.device, dtype=torch.float32)
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s = s.flatten(1)
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if a is not None:
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a = to_torch(a, device=self.device, dtype=torch.float32)
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a = a.flatten(1)
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s = torch.cat([s, a], dim=1)
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logits, h = self.preprocess(s)
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logits = self.last(logits)
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return logits
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class ActorProb(nn.Module):
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"""For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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"""
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def __init__(self, preprocess_net, action_shape, max_action,
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device='cpu', unbounded=False, hidden_layer_size=128):
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super().__init__()
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self.preprocess = preprocess_net
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self.device = device
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self.mu = nn.Linear(hidden_layer_size, np.prod(action_shape))
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self.sigma = nn.Parameter(torch.zeros(np.prod(action_shape), 1))
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self._max = max_action
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self._unbounded = unbounded
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def forward(self, s, state=None, **kwargs):
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"""s -> logits -> (mu, sigma)"""
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logits, h = self.preprocess(s, state)
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mu = self.mu(logits)
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if not self._unbounded:
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mu = self._max * torch.tanh(mu)
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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 RecurrentActorProb(nn.Module):
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"""For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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"""
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def __init__(self, layer_num, state_shape, action_shape,
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max_action, device='cpu', hidden_layer_size=128):
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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),
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hidden_size=hidden_layer_size,
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num_layers=layer_num, batch_first=True)
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self.mu = nn.Linear(hidden_layer_size, 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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"""Almost the same as :class:`~tianshou.utils.net.common.Recurrent`."""
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s = to_torch(s, device=self.device, dtype=torch.float32)
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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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s = s.unsqueeze(-2)
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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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"""For advanced usage (how to customize the network), please refer to
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:ref:`build_the_network`.
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"""
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def __init__(self, layer_num, state_shape,
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action_shape=0, device='cpu', hidden_layer_size=128):
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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),
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hidden_size=hidden_layer_size,
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num_layers=layer_num, batch_first=True)
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self.fc2 = nn.Linear(hidden_layer_size + np.prod(action_shape), 1)
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def forward(self, s, a=None):
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"""Almost the same as :class:`~tianshou.utils.net.common.Recurrent`."""
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s = to_torch(s, device=self.device, dtype=torch.float32)
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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.float32)
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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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