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
84 lines
3.0 KiB
Python
84 lines
3.0 KiB
Python
from typing import Any, Literal, cast
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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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import torch.nn.functional as F
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from tianshou.data import Batch, to_torch
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from tianshou.data.batch import BatchProtocol
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from tianshou.data.types import (
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ModelOutputBatchProtocol,
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ObsBatchProtocol,
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RolloutBatchProtocol,
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)
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from tianshou.policy import BasePolicy
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from tianshou.policy.base import TLearningRateScheduler
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class ImitationPolicy(BasePolicy):
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"""Implementation of vanilla imitation learning.
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:param actor: a model following the rules in
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:class:`~tianshou.policy.BasePolicy`. (s -> a)
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:param optim: for optimizing the model.
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:param action_space: Env's action_space.
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:param observation_space: Env's observation space.
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:param action_scaling: if True, scale the action from [-1, 1] to the range
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of action_space. Only used if the action_space is continuous.
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:param action_bound_method: method to bound action to range [-1, 1].
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Only used if the action_space is continuous.
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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.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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*,
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actor: torch.nn.Module,
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optim: torch.optim.Optimizer,
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action_space: gym.Space,
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observation_space: gym.Space | None = None,
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action_scaling: bool = False,
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action_bound_method: Literal["clip", "tanh"] | None = "clip",
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lr_scheduler: TLearningRateScheduler | None = None,
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) -> None:
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super().__init__(
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action_space=action_space,
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observation_space=observation_space,
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action_scaling=action_scaling,
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action_bound_method=action_bound_method,
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lr_scheduler=lr_scheduler,
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)
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self.actor = actor
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self.optim = optim
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def forward(
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self,
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batch: ObsBatchProtocol,
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state: dict | BatchProtocol | np.ndarray | None = None,
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**kwargs: Any,
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) -> ModelOutputBatchProtocol:
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logits, hidden = self.actor(batch.obs, state=state, info=batch.info)
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act = logits.max(dim=1)[1] if self.action_type == "discrete" else logits
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result = Batch(logits=logits, act=act, state=hidden)
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return cast(ModelOutputBatchProtocol, result)
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def learn(self, batch: RolloutBatchProtocol, *ags: Any, **kwargs: Any) -> dict[str, float]:
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self.optim.zero_grad()
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if self.action_type == "continuous": # regression
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act = self(batch).act
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act_target = to_torch(batch.act, dtype=torch.float32, device=act.device)
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loss = F.mse_loss(act, act_target)
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elif self.action_type == "discrete": # classification
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act = F.log_softmax(self(batch).logits, dim=-1)
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act_target = to_torch(batch.act, dtype=torch.long, device=act.device)
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loss = F.nll_loss(act, act_target)
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loss.backward()
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self.optim.step()
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return {"loss": loss.item()}
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