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280 行
10 KiB
280 行
10 KiB
from typing import Any, Dict, List, Tuple, Optional
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import numpy as np
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from mlagents.torch_utils import torch, default_device
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import copy
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from mlagents.trainers.action_info import ActionInfo
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from mlagents.trainers.behavior_id_utils import get_global_agent_id
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from mlagents.trainers.policy import Policy
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from mlagents_envs.base_env import DecisionSteps, BehaviorSpec
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from mlagents_envs.timers import timed
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from mlagents.trainers.settings import TrainerSettings
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from mlagents.trainers.trajectory import SplitObservations
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from mlagents.trainers.torch.networks import (
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SharedActorCritic,
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SeparateActorCritic,
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GlobalSteps,
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)
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from mlagents.trainers.torch.utils import ModelUtils
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EPSILON = 1e-7 # Small value to avoid divide by zero
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class TorchPolicy(Policy):
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def __init__(
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self,
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seed: int,
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behavior_spec: BehaviorSpec,
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trainer_settings: TrainerSettings,
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tanh_squash: bool = False,
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reparameterize: bool = False,
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separate_critic: bool = True,
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condition_sigma_on_obs: bool = True,
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):
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"""
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Policy that uses a multilayer perceptron to map the observations to actions. Could
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also use a CNN to encode visual input prior to the MLP. Supports discrete and
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continuous action spaces, as well as recurrent networks.
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:param seed: Random seed.
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:param behavior_spec: Assigned BehaviorSpec object.
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:param trainer_settings: Defined training parameters.
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:param load: Whether a pre-trained model will be loaded or a new one created.
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:param tanh_squash: Whether to use a tanh function on the continuous output,
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or a clipped output.
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:param reparameterize: Whether we are using the resampling trick to update the policy
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in continuous output.
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"""
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super().__init__(
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seed,
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behavior_spec,
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trainer_settings,
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tanh_squash,
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reparameterize,
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condition_sigma_on_obs,
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)
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self.global_step = (
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GlobalSteps()
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) # could be much simpler if TorchPolicy is nn.Module
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self.grads = None
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reward_signal_configs = trainer_settings.reward_signals
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reward_signal_names = [key.value for key, _ in reward_signal_configs.items()]
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self.stats_name_to_update_name = {
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"Losses/Value Loss": "value_loss",
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"Losses/Policy Loss": "policy_loss",
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}
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if separate_critic:
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ac_class = SeparateActorCritic
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else:
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ac_class = SharedActorCritic
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self.actor_critic = ac_class(
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observation_shapes=self.behavior_spec.observation_shapes,
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network_settings=trainer_settings.network_settings,
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act_type=behavior_spec.action_type,
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act_size=self.act_size,
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stream_names=reward_signal_names,
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conditional_sigma=self.condition_sigma_on_obs,
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tanh_squash=tanh_squash,
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)
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# Save the m_size needed for export
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self._export_m_size = self.m_size
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# m_size needed for training is determined by network, not trainer settings
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self.m_size = self.actor_critic.memory_size
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self.actor_critic.to(default_device())
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@property
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def export_memory_size(self) -> int:
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"""
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Returns the memory size of the exported ONNX policy. This only includes the memory
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of the Actor and not any auxillary networks.
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"""
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return self._export_m_size
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def _split_decision_step(
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self, decision_requests: DecisionSteps
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) -> Tuple[SplitObservations, np.ndarray]:
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vec_vis_obs = SplitObservations.from_observations(decision_requests.obs)
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mask = None
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if not self.use_continuous_act:
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mask = torch.ones([len(decision_requests), np.sum(self.act_size)])
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if decision_requests.action_mask is not None:
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mask = torch.as_tensor(
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1 - np.concatenate(decision_requests.action_mask, axis=1)
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)
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return vec_vis_obs, mask
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def update_normalization(self, vector_obs: np.ndarray) -> None:
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"""
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If this policy normalizes vector observations, this will update the norm values in the graph.
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:param vector_obs: The vector observations to add to the running estimate of the distribution.
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"""
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vector_obs = [torch.as_tensor(vector_obs)]
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if self.use_vec_obs and self.normalize:
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self.actor_critic.update_normalization(vector_obs)
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@timed
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def sample_actions(
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self,
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vec_obs: List[torch.Tensor],
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vis_obs: List[torch.Tensor],
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masks: Optional[torch.Tensor] = None,
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memories: Optional[torch.Tensor] = None,
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seq_len: int = 1,
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all_log_probs: bool = False,
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) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor, torch.Tensor]:
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"""
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:param vec_obs: List of vector observations.
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:param vis_obs: List of visual observations.
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:param masks: Loss masks for RNN, else None.
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:param memories: Input memories when using RNN, else None.
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:param seq_len: Sequence length when using RNN.
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:param all_log_probs: Returns (for discrete actions) a tensor of log probs, one for each action.
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:return: Tuple of actions, log probabilities (dependent on all_log_probs), entropies, and
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output memories, all as Torch Tensors.
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"""
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if memories is None:
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dists, memories = self.actor_critic.get_dists(
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vec_obs, vis_obs, masks, memories, seq_len
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)
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else:
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# If we're using LSTM. we need to execute the values to get the critic memories
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dists, _, memories = self.actor_critic.get_dist_and_value(
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vec_obs, vis_obs, masks, memories, seq_len
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)
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action_list = self.actor_critic.sample_action(dists)
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log_probs, entropies, all_logs = ModelUtils.get_probs_and_entropy(
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action_list, dists
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)
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actions = torch.stack(action_list, dim=-1)
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if self.use_continuous_act:
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actions = actions[:, :, 0]
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else:
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actions = actions[:, 0, :]
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return (actions, all_logs if all_log_probs else log_probs, entropies, memories)
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def evaluate_actions(
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self,
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vec_obs: torch.Tensor,
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vis_obs: torch.Tensor,
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actions: torch.Tensor,
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masks: Optional[torch.Tensor] = None,
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memories: Optional[torch.Tensor] = None,
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seq_len: int = 1,
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) -> Tuple[torch.Tensor, torch.Tensor, Dict[str, torch.Tensor]]:
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dists, value_heads, _ = self.actor_critic.get_dist_and_value(
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vec_obs, vis_obs, masks, memories, seq_len
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)
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action_list = [actions[..., i] for i in range(actions.shape[-1])]
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log_probs, entropies, _ = ModelUtils.get_probs_and_entropy(action_list, dists)
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return log_probs, entropies, value_heads
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@timed
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def evaluate(
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self, decision_requests: DecisionSteps, global_agent_ids: List[str]
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) -> Dict[str, Any]:
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"""
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Evaluates policy for the agent experiences provided.
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:param global_agent_ids:
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:param decision_requests: DecisionStep object containing inputs.
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:return: Outputs from network as defined by self.inference_dict.
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"""
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vec_vis_obs, masks = self._split_decision_step(decision_requests)
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vec_obs = [torch.as_tensor(vec_vis_obs.vector_observations)]
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vis_obs = [
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torch.as_tensor(vis_ob) for vis_ob in vec_vis_obs.visual_observations
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]
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memories = torch.as_tensor(self.retrieve_memories(global_agent_ids)).unsqueeze(
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0
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)
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run_out = {}
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with torch.no_grad():
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action, log_probs, entropy, memories = self.sample_actions(
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vec_obs, vis_obs, masks=masks, memories=memories
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)
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run_out["action"] = ModelUtils.to_numpy(action)
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run_out["pre_action"] = ModelUtils.to_numpy(action)
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# Todo - make pre_action difference
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run_out["log_probs"] = ModelUtils.to_numpy(log_probs)
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run_out["entropy"] = ModelUtils.to_numpy(entropy)
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run_out["learning_rate"] = 0.0
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if self.use_recurrent:
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run_out["memory_out"] = ModelUtils.to_numpy(memories).squeeze(0)
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return run_out
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def get_action(
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self, decision_requests: DecisionSteps, worker_id: int = 0
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) -> ActionInfo:
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"""
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Decides actions given observations information, and takes them in environment.
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:param worker_id:
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:param decision_requests: A dictionary of behavior names and DecisionSteps from environment.
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:return: an ActionInfo containing action, memories, values and an object
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to be passed to add experiences
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"""
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if len(decision_requests) == 0:
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return ActionInfo.empty()
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global_agent_ids = [
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get_global_agent_id(worker_id, int(agent_id))
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for agent_id in decision_requests.agent_id
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] # For 1-D array, the iterator order is correct.
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run_out = self.evaluate(
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decision_requests, global_agent_ids
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) # pylint: disable=assignment-from-no-return
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self.save_memories(global_agent_ids, run_out.get("memory_out"))
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return ActionInfo(
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action=run_out.get("action"),
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value=run_out.get("value"),
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outputs=run_out,
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agent_ids=list(decision_requests.agent_id),
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)
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@property
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def use_vis_obs(self):
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return self.vis_obs_size > 0
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@property
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def use_vec_obs(self):
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return self.vec_obs_size > 0
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def get_current_step(self):
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"""
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Gets current model step.
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:return: current model step.
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"""
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return self.global_step.current_step
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def set_step(self, step: int) -> int:
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"""
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Sets current model step to step without creating additional ops.
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:param step: Step to set the current model step to.
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:return: The step the model was set to.
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"""
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self.global_step.current_step = step
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return step
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def increment_step(self, n_steps):
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"""
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Increments model step.
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"""
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self.global_step.increment(n_steps)
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return self.get_current_step()
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def load_weights(self, values: List[np.ndarray]) -> None:
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self.actor_critic.load_state_dict(values)
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def init_load_weights(self) -> None:
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pass
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def get_weights(self) -> List[np.ndarray]:
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return copy.deepcopy(self.actor_critic.state_dict())
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def get_modules(self):
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return {"Policy": self.actor_critic, "global_step": self.global_step}
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