Unity 机器学习代理工具包 (ML-Agents) 是一个开源项目,它使游戏和模拟能够作为训练智能代理的环境。
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105 行
4.1 KiB

from typing import Dict, Optional, Tuple, List
from mlagents.torch_utils import torch
import numpy as np
from mlagents.trainers.buffer import AgentBuffer
from mlagents.trainers.trajectory import ObsUtil, TeamObsUtil
from mlagents.trainers.torch.components.bc.module import BCModule
from mlagents.trainers.torch.components.reward_providers import create_reward_provider
from mlagents.trainers.policy.torch_policy import TorchPolicy
from mlagents.trainers.optimizer import Optimizer
from mlagents.trainers.settings import TrainerSettings
from mlagents.trainers.torch.utils import ModelUtils
class TorchOptimizer(Optimizer):
def __init__(self, policy: TorchPolicy, trainer_settings: TrainerSettings):
super().__init__()
self.policy = policy
self.trainer_settings = trainer_settings
self.update_dict: Dict[str, torch.Tensor] = {}
self.value_heads: Dict[str, torch.Tensor] = {}
self.memory_in: torch.Tensor = None
self.memory_out: torch.Tensor = None
self.m_size: int = 0
self.global_step = torch.tensor(0)
self.bc_module: Optional[BCModule] = None
self.create_reward_signals(trainer_settings.reward_signals)
if trainer_settings.behavioral_cloning is not None:
self.bc_module = BCModule(
self.policy,
trainer_settings.behavioral_cloning,
policy_learning_rate=trainer_settings.hyperparameters.learning_rate,
default_batch_size=trainer_settings.hyperparameters.batch_size,
default_num_epoch=3,
)
def update(self, batch: AgentBuffer, num_sequences: int) -> Dict[str, float]:
pass
def create_reward_signals(self, reward_signal_configs):
"""
Create reward signals
:param reward_signal_configs: Reward signal config.
"""
for reward_signal, settings in reward_signal_configs.items():
# Name reward signals by string in case we have duplicates later
self.reward_signals[reward_signal.value] = create_reward_provider(
reward_signal, self.policy.behavior_spec, settings
)
def get_trajectory_value_estimates(
self,
batch: AgentBuffer,
next_obs: List[np.ndarray],
next_critic_obs: List[List[np.ndarray]],
done: bool,
) -> Tuple[Dict[str, np.ndarray], Dict[str, float]]:
n_obs = len(self.policy.behavior_spec.sensor_specs)
current_obs = ObsUtil.from_buffer(batch, n_obs)
# Convert to tensors
current_obs = [ModelUtils.list_to_tensor(obs) for obs in current_obs]
next_obs = [ModelUtils.list_to_tensor(obs) for obs in next_obs]
memory = torch.zeros([1, 1, self.policy.m_size])
next_obs = [obs.unsqueeze(0) for obs in next_obs]
critic_obs = TeamObsUtil.from_buffer(batch, n_obs)
critic_obs = [
[ModelUtils.list_to_tensor(obs) for obs in _teammate_obs]
for _teammate_obs in critic_obs
]
next_critic_obs = [
ModelUtils.list_to_tensor_list(_list_obs) for _list_obs in next_critic_obs
]
# Expand dimensions of next critic obs
next_critic_obs = [
[_obs.unsqueeze(0) for _obs in _list_obs] for _list_obs in next_critic_obs
]
memory = torch.zeros([1, 1, self.policy.m_size])
value_estimates, next_memory = self.policy.actor_critic.critic_pass(
current_obs,
memory,
sequence_length=batch.num_experiences,
critic_obs=critic_obs,
)
next_value_estimate, _ = self.policy.actor_critic.critic_pass(
next_obs, next_memory, sequence_length=1, critic_obs=next_critic_obs
)
for name, estimate in value_estimates.items():
value_estimates[name] = ModelUtils.to_numpy(estimate)
next_value_estimate[name] = ModelUtils.to_numpy(next_value_estimate[name])
if done:
for k in next_value_estimate:
if not self.reward_signals[k].ignore_done:
next_value_estimate[k] = 0.0
return value_estimates, next_value_estimate