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111 行
4.5 KiB
111 行
4.5 KiB
from typing import Dict, Optional, Tuple, List
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import torch
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import numpy as np
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from mlagents_envs.base_env import DecisionSteps
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from mlagents.trainers.buffer import AgentBuffer
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from mlagents.trainers.components.bc.module import BCModule
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from mlagents.trainers.torch.components.reward_providers import create_reward_provider
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from mlagents.trainers.policy.torch_policy import TorchPolicy
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from mlagents.trainers.optimizer import Optimizer
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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.utils import ModelUtils
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class TorchOptimizer(Optimizer): # pylint: disable=W0223
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def __init__(self, policy: TorchPolicy, trainer_settings: TrainerSettings):
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super().__init__()
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self.policy = policy
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self.trainer_settings = trainer_settings
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self.update_dict: Dict[str, torch.Tensor] = {}
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self.value_heads: Dict[str, torch.Tensor] = {}
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self.memory_in: torch.Tensor = None
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self.memory_out: torch.Tensor = None
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self.m_size: int = 0
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self.global_step = torch.tensor(0)
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self.bc_module: Optional[BCModule] = None
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self.create_reward_signals(trainer_settings.reward_signals)
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def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
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pass
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def create_reward_signals(self, reward_signal_configs):
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"""
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Create reward signals
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:param reward_signal_configs: Reward signal config.
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"""
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for reward_signal, settings in reward_signal_configs.items():
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# Name reward signals by string in case we have duplicates later
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self.reward_signals[reward_signal.value] = create_reward_provider(
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reward_signal, self.policy.behavior_spec, settings
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)
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def get_value_estimates(
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self, decision_requests: DecisionSteps, idx: int, done: bool
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) -> Dict[str, float]:
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"""
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Generates value estimates for bootstrapping.
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:param decision_requests:
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:param idx: Index in BrainInfo of agent.
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:param done: Whether or not this is the last element of the episode,
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in which case the value estimate will be 0.
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:return: The value estimate dictionary with key being the name of the reward signal
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and the value the corresponding value estimate.
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"""
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vec_vis_obs = SplitObservations.from_observations(decision_requests.obs)
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value_estimates = self.policy.actor_critic.critic_pass(
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np.expand_dims(vec_vis_obs.vector_observations[idx], 0),
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np.expand_dims(vec_vis_obs.visual_observations[idx], 0),
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)
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value_estimates = {k: float(v) for k, v in value_estimates.items()}
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# If we're done, reassign all of the value estimates that need terminal states.
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if done:
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for k in value_estimates:
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if not self.reward_signals[k].ignore_done:
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value_estimates[k] = 0.0
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return value_estimates
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def get_trajectory_value_estimates(
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self, batch: AgentBuffer, next_obs: List[np.ndarray], done: bool
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) -> Tuple[Dict[str, np.ndarray], Dict[str, float]]:
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vector_obs = [ModelUtils.list_to_tensor(batch["vector_obs"])]
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if self.policy.use_vis_obs:
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visual_obs = []
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for idx, _ in enumerate(
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self.policy.actor_critic.network_body.visual_encoders
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):
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visual_ob = ModelUtils.list_to_tensor(batch["visual_obs%d" % idx])
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visual_obs.append(visual_ob)
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else:
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visual_obs = []
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memory = torch.zeros([1, len(vector_obs[0]), self.policy.m_size])
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next_obs = np.concatenate(next_obs, axis=-1)
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next_obs = [ModelUtils.list_to_tensor(next_obs).unsqueeze(0)]
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next_memory = torch.zeros([1, 1, self.policy.m_size])
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value_estimates = self.policy.actor_critic.critic_pass(
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vector_obs, visual_obs, memory
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)
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next_value_estimate = self.policy.actor_critic.critic_pass(
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next_obs, next_obs, next_memory
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)
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for name, estimate in value_estimates.items():
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value_estimates[name] = estimate.detach().cpu().numpy()
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next_value_estimate[name] = next_value_estimate[name].detach().cpu().numpy()
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if done:
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for k in next_value_estimate:
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if not self.reward_signals[k].ignore_done:
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next_value_estimate[k] = 0.0
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return value_estimates, next_value_estimate
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