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).
170 lines
6.2 KiB
Python
170 lines
6.2 KiB
Python
import torch
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import numpy as np
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from copy import deepcopy
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from typing import Dict, Union, Optional
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from tianshou.policy import BasePolicy
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from tianshou.data import Batch, ReplayBuffer, to_torch_as, to_numpy
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class DQNPolicy(BasePolicy):
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"""Implementation of Deep Q Network. arXiv:1312.5602
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Implementation of Double Q-Learning. arXiv:1509.06461
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Implementation of Dueling DQN. arXiv:1511.06581 (the dueling DQN is
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implemented in the network side, not here)
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:param torch.nn.Module model: a model following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> logits)
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:param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
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:param float discount_factor: in [0, 1].
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:param int estimation_step: greater than 1, the number of steps to look
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ahead.
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:param int target_update_freq: the target network update frequency (``0``
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if you do not use the target network).
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:param bool reward_normalization: normalize the reward to Normal(0, 1),
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defaults to ``False``.
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.. seealso::
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Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
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explanation.
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"""
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def __init__(self,
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model: torch.nn.Module,
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optim: torch.optim.Optimizer,
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discount_factor: float = 0.99,
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estimation_step: int = 1,
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target_update_freq: int = 0,
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reward_normalization: bool = False,
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**kwargs) -> None:
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super().__init__(**kwargs)
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self.model = model
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self.optim = optim
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self.eps = 0
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assert 0 <= discount_factor <= 1, 'discount_factor should in [0, 1]'
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self._gamma = discount_factor
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assert estimation_step > 0, 'estimation_step should greater than 0'
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self._n_step = estimation_step
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self._target = target_update_freq > 0
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self._freq = target_update_freq
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self._cnt = 0
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if self._target:
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self.model_old = deepcopy(self.model)
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self.model_old.eval()
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self._rew_norm = reward_normalization
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def set_eps(self, eps: float) -> None:
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"""Set the eps for epsilon-greedy exploration."""
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self.eps = eps
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def train(self, mode=True) -> torch.nn.Module:
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"""Set the module in training mode, except for the target network."""
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self.training = mode
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self.model.train(mode)
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return self
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def sync_weight(self) -> None:
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"""Synchronize the weight for the target network."""
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self.model_old.load_state_dict(self.model.state_dict())
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def _target_q(self, buffer: ReplayBuffer,
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indice: np.ndarray) -> torch.Tensor:
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batch = buffer[indice] # batch.obs_next: s_{t+n}
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if self._target:
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# target_Q = Q_old(s_, argmax(Q_new(s_, *)))
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a = self(batch, input='obs_next', eps=0).act
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with torch.no_grad():
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target_q = self(
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batch, model='model_old', input='obs_next').logits
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target_q = target_q[np.arange(len(a)), a]
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else:
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with torch.no_grad():
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target_q = self(batch, input='obs_next').logits.max(dim=1)[0]
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return target_q
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def process_fn(self, batch: Batch, buffer: ReplayBuffer,
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indice: np.ndarray) -> Batch:
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"""Compute the n-step return for Q-learning targets. More details can
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be found at :meth:`~tianshou.policy.BasePolicy.compute_nstep_return`.
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"""
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batch = self.compute_nstep_return(
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batch, buffer, indice, self._target_q,
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self._gamma, self._n_step, self._rew_norm)
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return batch
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def forward(self, batch: Batch,
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state: Optional[Union[dict, Batch, np.ndarray]] = None,
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model: str = 'model',
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input: str = 'obs',
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eps: Optional[float] = None,
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**kwargs) -> Batch:
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"""Compute action over the given batch data. If you need to mask the
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action, please add a "mask" into batch.obs, for example, if we have an
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environment that has "0/1/2" three actions:
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::
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batch == Batch(
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obs=Batch(
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obs="original obs, with batch_size=1 for demonstration",
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mask=np.array([[False, True, False]]),
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# action 1 is available
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# action 0 and 2 are unavailable
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),
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...
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)
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:param float eps: in [0, 1], for epsilon-greedy exploration method.
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:return: A :class:`~tianshou.data.Batch` which has 3 keys:
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* ``act`` the action.
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* ``logits`` the network's raw output.
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* ``state`` the hidden state.
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.. seealso::
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Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
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more detailed explanation.
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"""
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model = getattr(self, model)
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obs = getattr(batch, input)
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obs_ = obs.obs if hasattr(obs, 'obs') else obs
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q, h = model(obs_, state=state, info=batch.info)
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act = to_numpy(q.max(dim=1)[1])
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has_mask = hasattr(obs, 'mask')
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if has_mask:
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# some of actions are masked, they cannot be selected
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q_ = to_numpy(q)
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q_[~obs.mask] = -np.inf
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act = q_.argmax(axis=1)
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# add eps to act
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if eps is None:
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eps = self.eps
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if not np.isclose(eps, 0):
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for i in range(len(q)):
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if np.random.rand() < eps:
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q_ = np.random.rand(*q[i].shape)
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if has_mask:
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q_[~obs.mask[i]] = -np.inf
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act[i] = q_.argmax()
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return Batch(logits=q, act=act, state=h)
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def learn(self, batch: Batch, **kwargs) -> Dict[str, float]:
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if self._target and self._cnt % self._freq == 0:
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self.sync_weight()
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self.optim.zero_grad()
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weight = batch.pop('weight', 1.)
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q = self(batch, eps=0.).logits
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q = q[np.arange(len(q)), batch.act]
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r = to_torch_as(batch.returns, q).flatten()
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td = r - q
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loss = (td.pow(2) * weight).mean()
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batch.weight = td # prio-buffer
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loss.backward()
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self.optim.step()
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self._cnt += 1
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return {'loss': loss.item()}
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