94bfb32cc1
1. add policy.eval() in all test scripts' "watch performance" 2. remove dict return support for collector preprocess_fn 3. add `__contains__` and `pop` in batch: `key in batch`, `batch.pop(key, deft)` 4. exact n_episode for a list of n_episode limitation and save fake data in cache_buffer when self.buffer is None (#184) 5. fix tensorboard logging: h-axis stands for env step instead of gradient step; add test results into tensorboard 6. add test_returns (both GAE and nstep) 7. change the type-checking order in batch.py and converter.py in order to meet the most often case first 8. fix shape inconsistency for torch.Tensor in replay buffer 9. remove `**kwargs` in ReplayBuffer 10. remove default value in batch.split() and add merge_last argument (#185) 11. improve nstep efficiency 12. add max_batchsize in onpolicy algorithms 13. potential bugfix for subproc.wait 14. fix RecurrentActorProb 15. improve the code-coverage (from 90% to 95%) and remove the dead code 16. fix some incorrect type annotation The above improvement also increases the training FPS: on my computer, the previous version is only ~1800 FPS and after that, it can reach ~2050 (faster than v0.2.4.post1).
132 lines
5.2 KiB
Python
132 lines
5.2 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 typing import List, Tuple, Union, Optional
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from tianshou.data import to_torch
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def miniblock(inp: int, oup: int,
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norm_layer: nn.modules.Module) -> List[nn.modules.Module]:
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ret = [nn.Linear(inp, oup)]
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if norm_layer is not None:
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ret += [norm_layer(oup)]
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ret += [nn.ReLU(inplace=True)]
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return ret
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class Net(nn.Module):
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"""Simple MLP backbone. For advanced usage (how to customize the network),
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please refer to :ref:`build_the_network`.
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:param bool concat: whether the input shape is concatenated by state_shape
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and action_shape. If it is True, ``action_shape`` is not the output
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shape, but affects the input shape.
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:param bool dueling: whether to use dueling network to calculate Q values
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(for Dueling DQN), defaults to False.
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:param nn.modules.Module norm_layer: use which normalization before ReLU,
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e.g., ``nn.LayerNorm`` and ``nn.BatchNorm1d``, defaults to None.
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"""
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def __init__(self, layer_num: int, state_shape: tuple,
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action_shape: Optional[Union[tuple, int]] = 0,
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device: Union[str, torch.device] = 'cpu',
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softmax: bool = False,
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concat: bool = False,
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hidden_layer_size: int = 128,
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dueling: Optional[Tuple[int, int]] = None,
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norm_layer: Optional[nn.modules.Module] = None):
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super().__init__()
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self.device = device
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self.dueling = dueling
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self.softmax = softmax
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input_size = np.prod(state_shape)
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if concat:
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input_size += np.prod(action_shape)
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self.model = miniblock(input_size, hidden_layer_size, norm_layer)
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for i in range(layer_num):
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self.model += miniblock(hidden_layer_size,
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hidden_layer_size, norm_layer)
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if self.dueling is None:
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if action_shape and not concat:
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self.model += [nn.Linear(hidden_layer_size,
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np.prod(action_shape))]
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else: # dueling DQN
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assert isinstance(self.dueling, tuple) and len(self.dueling) == 2
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q_layer_num, v_layer_num = self.dueling
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self.Q, self.V = [], []
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for i in range(q_layer_num):
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self.Q += miniblock(hidden_layer_size,
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hidden_layer_size, norm_layer)
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for i in range(v_layer_num):
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self.V += miniblock(hidden_layer_size,
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hidden_layer_size, norm_layer)
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if action_shape and not concat:
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self.Q += [nn.Linear(hidden_layer_size, np.prod(action_shape))]
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self.V += [nn.Linear(hidden_layer_size, 1)]
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self.Q = nn.Sequential(*self.Q)
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self.V = nn.Sequential(*self.V)
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self.model = nn.Sequential(*self.model)
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def forward(self, s, state=None, info={}):
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"""s -> flatten -> logits"""
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s = to_torch(s, device=self.device, dtype=torch.float32)
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s = s.reshape(s.size(0), -1)
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logits = self.model(s)
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if self.dueling is not None: # Dueling DQN
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q, v = self.Q(logits), self.V(logits)
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logits = q - q.mean(dim=1, keepdim=True) + v
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if self.softmax:
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logits = torch.softmax(logits, dim=-1)
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return logits, state
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class Recurrent(nn.Module):
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"""Simple Recurrent network based on LSTM. For advanced usage (how to
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customize the network), please refer to :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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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=hidden_layer_size,
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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.fc1 = nn.Linear(np.prod(state_shape), hidden_layer_size)
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self.fc2 = nn.Linear(hidden_layer_size, np.prod(action_shape))
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def forward(self, s, state=None, info={}):
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"""In the evaluation mode, s should be with shape ``[bsz, dim]``; in
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the training mode, s should be with shape ``[bsz, len, dim]``. See the
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code and comment for more detail.
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"""
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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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s = self.fc1(s)
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self.nn.flatten_parameters()
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if state is None:
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s, (h, c) = self.nn(s)
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else:
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# we store the stack data in [bsz, len, ...] format
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# but pytorch rnn needs [len, bsz, ...]
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s, (h, c) = self.nn(s, (state['h'].transpose(0, 1).contiguous(),
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state['c'].transpose(0, 1).contiguous()))
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s = self.fc2(s[:, -1])
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# please ensure the first dim is batch size: [bsz, len, ...]
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return s, {'h': h.transpose(0, 1).detach(),
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'c': c.transpose(0, 1).detach()}
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