145 lines
5.5 KiB
Python
145 lines
5.5 KiB
Python
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, to_torch_as
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from tianshou.policy import DQNPolicy
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from tianshou.utils.net.common import BranchingNet
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class BranchingDQNPolicy(DQNPolicy):
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"""Implementation of the Branching dual Q network arXiv:1711.08946.
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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 network. 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: BranchingNet,
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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__(
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model, optim, discount_factor, estimation_step, target_update_freq,
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reward_normalization, is_double
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)
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assert estimation_step == 1, "N-step bigger than one is not supported by BDQ"
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self.max_action_num = model.action_per_branch
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self.num_branches = model.num_branches
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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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act = np.expand_dims(self(batch, input="obs_next").act, -1)
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act = to_torch(act, dtype=torch.long, device=target_q.device)
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else:
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act = target_q.max(-1).indices.unsqueeze(-1)
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return torch.gather(target_q, -1, act).squeeze()
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def _compute_return(
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self,
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batch: Batch,
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buffer: ReplayBuffer,
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indice: np.ndarray,
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gamma: float = 0.99,
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) -> Batch:
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rew = batch.rew
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with torch.no_grad():
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target_q_torch = self._target_q(buffer, indice) # (bsz, ?)
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target_q = to_numpy(target_q_torch)
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end_flag = buffer.done.copy()
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end_flag[buffer.unfinished_index()] = True
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end_flag = end_flag[indice]
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mean_target_q = np.mean(target_q, -1) if len(target_q.shape) > 1 else target_q
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_target_q = rew + gamma * mean_target_q * (1 - end_flag)
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target_q = np.repeat(_target_q[..., None], self.num_branches, axis=-1)
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target_q = np.repeat(target_q[..., None], self.max_action_num, axis=-1)
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batch.returns = to_torch_as(target_q, target_q_torch)
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if hasattr(batch, "weight"): # prio buffer update
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batch.weight = to_torch_as(batch.weight, target_q_torch)
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return batch
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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 1-step return for BDQ targets."""
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return self._compute_return(batch, buffer, indices)
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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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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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act = to_numpy(logits.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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act = to_torch(batch.act, dtype=torch.long, device=batch.returns.device)
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q = self(batch).logits
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act_mask = torch.zeros_like(q)
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act_mask = act_mask.scatter_(-1, act.unsqueeze(-1), 1)
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act_q = q * act_mask
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returns = batch.returns
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returns = returns * act_mask
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td_error = returns - act_q
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loss = (td_error.pow(2).sum(-1).mean(-1) * weight).mean()
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batch.weight = td_error.sum(-1).sum(-1) # 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(
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self,
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act: Union[np.ndarray, Batch],
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batch: Batch,
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) -> 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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rand_act = np.random.randint(
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low=0, high=self.max_action_num, size=(bsz, act.shape[-1])
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)
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if hasattr(batch.obs, "mask"):
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rand_act += batch.obs.mask
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act[rand_mask] = rand_act[rand_mask]
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return act
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