from copy import deepcopy
from typing import Any, Dict, Optional, Union
import numpy as np
import torch
from tianshou.data import Batch, ReplayBuffer, to_numpy, to_torch_as
from tianshou.policy import BasePolicy
[docs]class DQNPolicy(BasePolicy):
"""Implementation of Deep Q Network. arXiv:1312.5602.
Implementation of Double Q-Learning. arXiv:1509.06461.
Implementation of Dueling DQN. arXiv:1511.06581 (the dueling DQN is
implemented in the network side, not here).
:param torch.nn.Module model: a model following the rules in
:class:`~tianshou.policy.BasePolicy`. (s -> logits)
:param torch.optim.Optimizer optim: a torch.optim for optimizing the model.
:param float discount_factor: in [0, 1].
:param int estimation_step: the number of steps to look ahead. Default to 1.
:param int target_update_freq: the target network update frequency (0 if
you do not use the target network). Default to 0.
:param bool reward_normalization: normalize the reward to Normal(0, 1).
Default to False.
:param bool is_double: use double dqn. Default to True.
.. seealso::
Please refer to :class:`~tianshou.policy.BasePolicy` for more detailed
explanation.
"""
def __init__(
self,
model: torch.nn.Module,
optim: torch.optim.Optimizer,
discount_factor: float = 0.99,
estimation_step: int = 1,
target_update_freq: int = 0,
reward_normalization: bool = False,
is_double: bool = True,
**kwargs: Any,
) -> None:
super().__init__(**kwargs)
self.model = model
self.optim = optim
self.eps = 0.0
assert 0.0 <= discount_factor <= 1.0, "discount factor should be in [0, 1]"
self._gamma = discount_factor
assert estimation_step > 0, "estimation_step should be greater than 0"
self._n_step = estimation_step
self._target = target_update_freq > 0
self._freq = target_update_freq
self._iter = 0
if self._target:
self.model_old = deepcopy(self.model)
self.model_old.eval()
self._rew_norm = reward_normalization
self._is_double = is_double
[docs] def set_eps(self, eps: float) -> None:
"""Set the eps for epsilon-greedy exploration."""
self.eps = eps
[docs] def train(self, mode: bool = True) -> "DQNPolicy":
"""Set the module in training mode, except for the target network."""
self.training = mode
self.model.train(mode)
return self
[docs] def sync_weight(self) -> None:
"""Synchronize the weight for the target network."""
self.model_old.load_state_dict(self.model.state_dict())
def _target_q(self, buffer: ReplayBuffer, indices: np.ndarray) -> torch.Tensor:
batch = buffer[indices] # batch.obs_next: s_{t+n}
result = self(batch, input="obs_next")
if self._target:
# target_Q = Q_old(s_, argmax(Q_new(s_, *)))
target_q = self(batch, model="model_old", input="obs_next").logits
else:
target_q = result.logits
if self._is_double:
return target_q[np.arange(len(result.act)), result.act]
else: # Nature DQN, over estimate
return target_q.max(dim=1)[0]
[docs] def process_fn(
self, batch: Batch, buffer: ReplayBuffer, indices: np.ndarray
) -> Batch:
"""Compute the n-step return for Q-learning targets.
More details can be found at
:meth:`~tianshou.policy.BasePolicy.compute_nstep_return`.
"""
batch = self.compute_nstep_return(
batch, buffer, indices, self._target_q, self._gamma, self._n_step,
self._rew_norm
)
return batch
[docs] def compute_q_value(
self, logits: torch.Tensor, mask: Optional[np.ndarray]
) -> torch.Tensor:
"""Compute the q value based on the network's raw output and action mask."""
if mask is not None:
# the masked q value should be smaller than logits.min()
min_value = logits.min() - logits.max() - 1.0
logits = logits + to_torch_as(1 - mask, logits) * min_value
return logits
[docs] def forward(
self,
batch: Batch,
state: Optional[Union[dict, Batch, np.ndarray]] = None,
model: str = "model",
input: str = "obs",
**kwargs: Any,
) -> Batch:
"""Compute action over the given batch data.
If you need to mask the action, please add a "mask" into batch.obs, for
example, if we have an environment that has "0/1/2" three actions:
::
batch == Batch(
obs=Batch(
obs="original obs, with batch_size=1 for demonstration",
mask=np.array([[False, True, False]]),
# action 1 is available
# action 0 and 2 are unavailable
),
...
)
:param float eps: in [0, 1], for epsilon-greedy exploration method.
:return: A :class:`~tianshou.data.Batch` which has 3 keys:
* ``act`` the action.
* ``logits`` the network's raw output.
* ``state`` the hidden state.
.. seealso::
Please refer to :meth:`~tianshou.policy.BasePolicy.forward` for
more detailed explanation.
"""
model = getattr(self, model)
obs = batch[input]
obs_next = obs.obs if hasattr(obs, "obs") else obs
logits, hidden = model(obs_next, state=state, info=batch.info)
q = self.compute_q_value(logits, getattr(obs, "mask", None))
if not hasattr(self, "max_action_num"):
self.max_action_num = q.shape[1]
act = to_numpy(q.max(dim=1)[1])
return Batch(logits=logits, act=act, state=hidden)
[docs] def learn(self, batch: Batch, **kwargs: Any) -> Dict[str, float]:
if self._target and self._iter % self._freq == 0:
self.sync_weight()
self.optim.zero_grad()
weight = batch.pop("weight", 1.0)
q = self(batch).logits
q = q[np.arange(len(q)), batch.act]
returns = to_torch_as(batch.returns.flatten(), q)
td_error = returns - q
loss = (td_error.pow(2) * weight).mean()
batch.weight = td_error # prio-buffer
loss.backward()
self.optim.step()
self._iter += 1
return {"loss": loss.item()}
[docs] def exploration_noise(self, act: Union[np.ndarray, Batch],
batch: Batch) -> Union[np.ndarray, Batch]:
if isinstance(act, np.ndarray) and not np.isclose(self.eps, 0.0):
bsz = len(act)
rand_mask = np.random.rand(bsz) < self.eps
q = np.random.rand(bsz, self.max_action_num) # [0, 1]
if hasattr(batch.obs, "mask"):
q += batch.obs.mask
rand_act = q.argmax(axis=1)
act[rand_mask] = rand_act[rand_mask]
return act