3a1bc18add
This PR adds a new method for getting actions from an env's observation and info. This is useful for standard inference and stands in contrast to batch-based methods that are currently used in training and evaluation. Without this, users have to do some kind of gymnastics to actually perform inference with a trained policy. I have also added a test for the new method. In future PRs, this method should be included in the examples (in the the "watch" section). To add this required improving multiple typing things and, importantly, _simplifying the signature of `forward` in many policies!_ This is a **breaking change**, but it will likely affect no users. The `input` parameter of forward was a rather hacky mechanism, I believe it is good that it's gone now. It will also help with #948 . The main functional change is the addition of `compute_action` to `BasePolicy`. Other minor changes: - improvements in typing - updated PR and Issue templates - Improved handling of `max_action_num` Closes #981
128 lines
5.2 KiB
Python
128 lines
5.2 KiB
Python
from typing import Any
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import gymnasium as gym
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import numpy as np
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import torch
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from tianshou.data import Batch, ReplayBuffer
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from tianshou.data.types import RolloutBatchProtocol
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from tianshou.policy import DQNPolicy
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from tianshou.policy.base import TLearningRateScheduler
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class C51Policy(DQNPolicy):
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"""Implementation of Categorical Deep Q-Network. arXiv:1707.06887.
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:param model: a model following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> logits)
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:param optim: a torch.optim for optimizing the model.
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:param discount_factor: in [0, 1].
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:param num_atoms: the number of atoms in the support set of the
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value distribution. Default to 51.
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:param v_min: the value of the smallest atom in the support set.
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Default to -10.0.
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:param v_max: the value of the largest atom in the support set.
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Default to 10.0.
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:param estimation_step: the number of steps to look ahead.
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:param target_update_freq: the target network update frequency (0 if
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you do not use the target network).
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:param reward_normalization: normalize the **returns** to Normal(0, 1).
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TODO: rename to return_normalization?
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:param is_double: use double dqn.
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:param clip_loss_grad: clip the gradient of the loss in accordance
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with nature14236; this amounts to using the Huber loss instead of
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the MSE loss.
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:param observation_space: Env's observation space.
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:param lr_scheduler: if not None, will be called in `policy.update()`.
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.. seealso::
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Please refer to :class:`~tianshou.policy.DQNPolicy` 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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*,
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model: torch.nn.Module,
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optim: torch.optim.Optimizer,
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action_space: gym.spaces.Discrete,
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discount_factor: float = 0.99,
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num_atoms: int = 51,
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v_min: float = -10.0,
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v_max: float = 10.0,
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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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clip_loss_grad: bool = False,
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observation_space: gym.Space | None = None,
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lr_scheduler: TLearningRateScheduler | None = None,
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) -> None:
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assert num_atoms > 1, f"num_atoms should be greater than 1 but got: {num_atoms}"
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assert v_min < v_max, f"v_max should be larger than v_min, but got {v_min=} and {v_max=}"
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super().__init__(
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model=model,
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optim=optim,
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action_space=action_space,
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discount_factor=discount_factor,
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estimation_step=estimation_step,
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target_update_freq=target_update_freq,
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reward_normalization=reward_normalization,
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is_double=is_double,
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clip_loss_grad=clip_loss_grad,
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observation_space=observation_space,
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lr_scheduler=lr_scheduler,
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)
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self._num_atoms = num_atoms
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self._v_min = v_min
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self._v_max = v_max
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self.support = torch.nn.Parameter(
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torch.linspace(self._v_min, self._v_max, self._num_atoms),
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requires_grad=False,
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)
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self.delta_z = (v_max - v_min) / (num_atoms - 1)
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def _target_q(self, buffer: ReplayBuffer, indices: np.ndarray) -> torch.Tensor:
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return self.support.repeat(len(indices), 1) # shape: [bsz, num_atoms]
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def compute_q_value(self, logits: torch.Tensor, mask: np.ndarray | None) -> torch.Tensor:
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return super().compute_q_value((logits * self.support).sum(2), mask)
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def _target_dist(self, batch: RolloutBatchProtocol) -> torch.Tensor:
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obs_next_batch = Batch(obs=batch.obs_next, info=[None] * len(batch))
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if self._target:
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act = self(obs_next_batch).act
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next_dist = self(obs_next_batch, model="model_old").logits
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else:
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next_batch = self(obs_next_batch)
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act = next_batch.act
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next_dist = next_batch.logits
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next_dist = next_dist[np.arange(len(act)), act, :]
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target_support = batch.returns.clamp(self._v_min, self._v_max)
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# An amazing trick for calculating the projection gracefully.
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# ref: https://github.com/ShangtongZhang/DeepRL
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target_dist = (
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1 - (target_support.unsqueeze(1) - self.support.view(1, -1, 1)).abs() / self.delta_z
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).clamp(0, 1) * next_dist.unsqueeze(1)
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return target_dist.sum(-1)
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def learn(self, batch: RolloutBatchProtocol, *args: Any, **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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with torch.no_grad():
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target_dist = self._target_dist(batch)
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weight = batch.pop("weight", 1.0)
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curr_dist = self(batch).logits
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act = batch.act
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curr_dist = curr_dist[np.arange(len(act)), act, :]
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cross_entropy = -(target_dist * torch.log(curr_dist + 1e-8)).sum(1)
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loss = (cross_entropy * weight).mean()
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# ref: https://github.com/Kaixhin/Rainbow/blob/master/agent.py L94-100
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batch.weight = cross_entropy.detach() # 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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