189 lines
6.9 KiB
Python
189 lines
6.9 KiB
Python
from copy import deepcopy
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from typing import Any, Dict, Optional, Union
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import numpy as np
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import torch
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from tianshou.data import Batch, ReplayBuffer, to_numpy, to_torch_as
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from tianshou.policy import BasePolicy
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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: the number of steps to look ahead. Default to 1.
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:param int target_update_freq: the target network update frequency (0 if
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you do not use the target network). Default to 0.
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:param bool reward_normalization: normalize the reward to Normal(0, 1).
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Default to False.
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:param bool is_double: use double dqn. Default to True.
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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__(
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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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is_double: bool = True,
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**kwargs: Any,
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) -> 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 = 0.0
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assert 0.0 <= discount_factor <= 1.0, "discount factor should be in [0, 1]"
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self._gamma = discount_factor
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assert estimation_step > 0, "estimation_step should be 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._iter = 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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self._is_double = is_double
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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: bool = True) -> "DQNPolicy":
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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, indices: np.ndarray) -> torch.Tensor:
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batch = buffer[indices] # batch.obs_next: s_{t+n}
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result = self(batch, input="obs_next")
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if self._target:
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# target_Q = Q_old(s_, argmax(Q_new(s_, *)))
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target_q = self(batch, model="model_old", input="obs_next").logits
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else:
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target_q = result.logits
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if self._is_double:
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return target_q[np.arange(len(result.act)), result.act]
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else: # Nature DQN, over estimate
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return target_q.max(dim=1)[0]
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def process_fn(
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self, batch: Batch, buffer: ReplayBuffer, indices: np.ndarray
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) -> Batch:
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"""Compute the n-step return for Q-learning targets.
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More details can be found at
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: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, indices, self._target_q, self._gamma, self._n_step,
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self._rew_norm
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)
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return batch
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def compute_q_value(
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self, logits: torch.Tensor, mask: Optional[np.ndarray]
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) -> torch.Tensor:
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"""Compute the q value based on the network's raw output and action mask."""
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if mask is not None:
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# the masked q value should be smaller than logits.min()
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min_value = logits.min() - logits.max() - 1.0
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logits = logits + to_torch_as(1 - mask, logits) * min_value
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return logits
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def forward(
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self,
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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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**kwargs: Any,
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) -> Batch:
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"""Compute action over the given batch data.
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If you need to mask the action, please add a "mask" into batch.obs, for
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example, if we have an 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 = batch[input]
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obs_next = obs.obs if hasattr(obs, "obs") else obs
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logits, hidden = model(obs_next, state=state, info=batch.info)
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q = self.compute_q_value(logits, getattr(obs, "mask", None))
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if not hasattr(self, "max_action_num"):
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self.max_action_num = q.shape[1]
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act = to_numpy(q.max(dim=1)[1])
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return Batch(logits=logits, act=act, state=hidden)
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def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
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if self._target and self._iter % 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", 1.0)
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q = self(batch).logits
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q = q[np.arange(len(q)), batch.act]
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returns = to_torch_as(batch.returns.flatten(), q)
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td_error = returns - q
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loss = (td_error.pow(2) * weight).mean()
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batch.weight = td_error # prio-buffer
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loss.backward()
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self.optim.step()
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self._iter += 1
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return {"loss": loss.item()}
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def exploration_noise(self, act: Union[np.ndarray, Batch],
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batch: Batch) -> Union[np.ndarray, Batch]:
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if isinstance(act, np.ndarray) and not np.isclose(self.eps, 0.0):
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bsz = len(act)
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rand_mask = np.random.rand(bsz) < self.eps
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q = np.random.rand(bsz, self.max_action_num) # [0, 1]
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if hasattr(batch.obs, "mask"):
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q += batch.obs.mask
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rand_act = q.argmax(axis=1)
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act[rand_mask] = rand_act[rand_mask]
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return act
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