mirror of
https://github.com/saymrwulf/stable-baselines3.git
synced 2026-07-28 20:11:31 +00:00
500 lines
23 KiB
Python
500 lines
23 KiB
Python
import time
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from typing import Any, Callable, Dict, List, Optional, Tuple, Type, Union
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import numpy as np
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import torch as th
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from torch.nn import functional as F
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from stable_baselines3.common import logger
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from stable_baselines3.common.buffers import ReplayBuffer
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from stable_baselines3.common.callbacks import BaseCallback
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from stable_baselines3.common.noise import ActionNoise
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from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
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from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, RolloutReturn
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from stable_baselines3.common.utils import polyak_update, safe_mean
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from stable_baselines3.common.vec_env import VecEnv
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from stable_baselines3.td3.policies import TD3Policy
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class TD3(OffPolicyAlgorithm):
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"""
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Twin Delayed DDPG (TD3)
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Addressing Function Approximation Error in Actor-Critic Methods.
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Original implementation: https://github.com/sfujim/TD3
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Paper: https://arxiv.org/abs/1802.09477
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Introduction to TD3: https://spinningup.openai.com/en/latest/algorithms/td3.html
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:param policy: (TD3Policy or str) The policy model to use (MlpPolicy, CnnPolicy, ...)
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:param env: (GymEnv or str) The environment to learn from (if registered in Gym, can be str)
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:param learning_rate: (float or callable) learning rate for adam optimizer,
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the same learning rate will be used for all networks (Q-Values, Actor and Value function)
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it can be a function of the current progress remaining (from 1 to 0)
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:param buffer_size: (int) size of the replay buffer
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:param learning_starts: (int) how many steps of the model to collect transitions for before learning starts
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:param batch_size: (int) Minibatch size for each gradient update
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:param tau: (float) the soft update coefficient ("Polyak update", between 0 and 1)
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:param gamma: (float) the discount factor
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:param train_freq: (int) Update the model every ``train_freq`` steps. Set to `-1` to disable.
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:param gradient_steps: (int) How many gradient steps to do after each rollout
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(see ``train_freq`` and ``n_episodes_rollout``)
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Set to ``-1`` means to do as many gradient steps as steps done in the environment
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during the rollout.
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:param n_episodes_rollout: (int) Update the model every ``n_episodes_rollout`` episodes.
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Note that this cannot be used at the same time as ``train_freq``. Set to `-1` to disable.
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:param action_noise: (ActionNoise) the action noise type (None by default), this can help
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for hard exploration problem. Cf common.noise for the different action noise type.
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:param optimize_memory_usage: (bool) Enable a memory efficient variant of the replay buffer
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at a cost of more complexity.
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See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
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:param policy_delay: (int) Policy and target networks will only be updated once every policy_delay steps
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per training steps. The Q values will be updated policy_delay more often (update every training step).
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:param target_policy_noise: (float) Standard deviation of Gaussian noise added to target policy
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(smoothing noise)
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:param target_noise_clip: (float) Limit for absolute value of target policy smoothing noise.
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:param use_sde: (bool) Whether to use State Dependent Exploration (SDE)
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instead of action noise exploration (default: False)
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:param sde_sample_freq: (int) Sample a new noise matrix every n steps when using SDE
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Default: -1 (only sample at the beginning of the rollout)
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:param sde_max_grad_norm: (float)
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:param sde_ent_coef: (float)
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:param sde_log_std_scheduler: (callable)
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:param use_sde_at_warmup: (bool) Whether to use SDE instead of uniform sampling
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during the warm up phase (before learning starts)
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:param create_eval_env: (bool) Whether to create a second environment that will be
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used for evaluating the agent periodically. (Only available when passing string for the environment)
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:param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
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:param verbose: (int) the verbosity level: 0 no output, 1 info, 2 debug
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:param seed: (int) Seed for the pseudo random generators
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:param device: (str or th.device) Device (cpu, cuda, ...) on which the code should be run.
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Setting it to auto, the code will be run on the GPU if possible.
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:param _init_setup_model: (bool) Whether or not to build the network at the creation of the instance
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"""
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def __init__(
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self,
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policy: Union[str, Type[TD3Policy]],
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env: Union[GymEnv, str],
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learning_rate: Union[float, Callable] = 1e-3,
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buffer_size: int = int(1e6),
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learning_starts: int = 100,
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batch_size: int = 100,
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tau: float = 0.005,
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gamma: float = 0.99,
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train_freq: int = -1,
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gradient_steps: int = -1,
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n_episodes_rollout: int = 1,
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action_noise: Optional[ActionNoise] = None,
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optimize_memory_usage: bool = False,
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policy_delay: int = 2,
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target_policy_noise: float = 0.2,
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target_noise_clip: float = 0.5,
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use_sde: bool = False,
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sde_sample_freq: int = -1,
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sde_max_grad_norm: float = 1,
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sde_ent_coef: float = 0.0,
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sde_log_std_scheduler: Optional[Callable] = None,
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use_sde_at_warmup: bool = False,
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tensorboard_log: Optional[str] = None,
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create_eval_env: bool = False,
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policy_kwargs: Dict[str, Any] = None,
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verbose: int = 0,
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seed: Optional[int] = None,
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device: Union[th.device, str] = "auto",
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_init_setup_model: bool = True,
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):
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super(TD3, self).__init__(
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policy,
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env,
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TD3Policy,
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learning_rate,
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buffer_size,
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learning_starts,
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batch_size,
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tau,
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gamma,
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train_freq,
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gradient_steps,
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n_episodes_rollout,
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action_noise=action_noise,
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policy_kwargs=policy_kwargs,
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tensorboard_log=tensorboard_log,
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verbose=verbose,
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device=device,
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create_eval_env=create_eval_env,
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seed=seed,
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use_sde=use_sde,
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sde_sample_freq=sde_sample_freq,
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use_sde_at_warmup=use_sde_at_warmup,
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optimize_memory_usage=optimize_memory_usage,
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)
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self.policy_delay = policy_delay
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self.target_noise_clip = target_noise_clip
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self.target_policy_noise = target_policy_noise
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# State Dependent Exploration
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self.sde_max_grad_norm = sde_max_grad_norm
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self.sde_ent_coef = sde_ent_coef
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self.sde_log_std_scheduler = sde_log_std_scheduler
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self.on_policy_exploration = True
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self.sde_vf = None
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if _init_setup_model:
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self._setup_model()
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def _setup_model(self) -> None:
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super(TD3, self)._setup_model()
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self._create_aliases()
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def _create_aliases(self) -> None:
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self.actor = self.policy.actor
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self.actor_target = self.policy.actor_target
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self.critic = self.policy.critic
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self.critic_target = self.policy.critic_target
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self.vf_net = self.policy.vf_net
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def train(self, gradient_steps: int, batch_size: int = 100) -> None:
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# Update learning rate according to lr schedule
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self._update_learning_rate([self.actor.optimizer, self.critic.optimizer])
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for gradient_step in range(gradient_steps):
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# Sample replay buffer
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replay_data = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
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with th.no_grad():
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# Select action according to policy and add clipped noise
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noise = replay_data.actions.clone().data.normal_(0, self.target_policy_noise)
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noise = noise.clamp(-self.target_noise_clip, self.target_noise_clip)
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next_actions = (self.actor_target(replay_data.next_observations) + noise).clamp(-1, 1)
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# Compute the target Q value: min over all critics targets
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targets = th.cat(self.critic_target(replay_data.next_observations, next_actions), dim=1)
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target_q, _ = th.min(targets, dim=1, keepdim=True)
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target_q = replay_data.rewards + (1 - replay_data.dones) * self.gamma * target_q
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# Get current Q estimates for each critic network
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current_q_esimates = self.critic(replay_data.observations, replay_data.actions)
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# Compute critic loss
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critic_loss = sum([F.mse_loss(current_q, target_q) for current_q in current_q_esimates])
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# Optimize the critics
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self.critic.optimizer.zero_grad()
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critic_loss.backward()
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self.critic.optimizer.step()
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# Delayed policy updates
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if gradient_step % self.policy_delay == 0:
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# Compute actor loss
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actor_loss = -self.critic.q1_forward(replay_data.observations, self.actor(replay_data.observations)).mean()
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# Optimize the actor
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self.actor.optimizer.zero_grad()
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actor_loss.backward()
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self.actor.optimizer.step()
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polyak_update(self.critic.parameters(), self.critic_target.parameters(), self.tau)
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polyak_update(self.actor.parameters(), self.actor_target.parameters(), self.tau)
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self._n_updates += gradient_steps
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logger.record("train/n_updates", self._n_updates, exclude="tensorboard")
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def train_sde(self) -> None:
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# Update optimizer learning rate
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# self._update_learning_rate(self.policy.optimizer)
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# Unpack
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obs, action, advantage, returns = [
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self.rollout_data[key] for key in ["observations", "actions", "advantage", "returns"]
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]
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log_prob, entropy = self.actor.evaluate_actions(obs, action)
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values = self.vf_net(obs).flatten()
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# Normalize advantage
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# if self.normalize_advantage:
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# advantage = (advantage - advantage.mean()) / (advantage.std() + 1e-8)
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# Value loss using the TD(gae_lambda) target
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value_loss = F.mse_loss(returns, values)
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# A2C loss
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policy_loss = -(advantage * log_prob).mean() # pytype: disable=attribute-error
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# Entropy loss favor exploration
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if entropy is None:
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# Approximate entropy when no analytical form
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entropy_loss = -log_prob.mean()
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else:
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entropy_loss = -th.mean(entropy)
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vf_coef = 0.5
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loss = policy_loss + self.sde_ent_coef * entropy_loss + vf_coef * value_loss
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# Optimization step
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self.actor.sde_optimizer.zero_grad()
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loss.backward()
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assert not th.isnan(log_prob).any(), log_prob
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assert not th.isnan(entropy).any()
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assert not th.isnan(self.actor.log_std.grad).any()
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assert not th.isnan(self.actor.log_std).any()
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# Clip grad norm
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th.nn.utils.clip_grad_norm_([self.actor.log_std], self.sde_max_grad_norm)
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self.actor.sde_optimizer.step()
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del self.rollout_data
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def learn(
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self,
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total_timesteps: int,
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callback: MaybeCallback = None,
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log_interval: int = 4,
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eval_env: Optional[GymEnv] = None,
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eval_freq: int = -1,
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n_eval_episodes: int = 5,
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tb_log_name: str = "TD3",
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eval_log_path: Optional[str] = None,
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reset_num_timesteps: bool = True,
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) -> OffPolicyAlgorithm:
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total_timesteps, callback = self._setup_learn(
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total_timesteps, eval_env, callback, eval_freq, n_eval_episodes, eval_log_path, reset_num_timesteps, tb_log_name
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)
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callback.on_training_start(locals(), globals())
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while self.num_timesteps < total_timesteps:
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rollout = self.collect_rollouts(
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self.env,
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n_episodes=self.n_episodes_rollout,
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n_steps=self.train_freq,
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action_noise=self.action_noise,
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callback=callback,
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learning_starts=self.learning_starts,
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replay_buffer=self.replay_buffer,
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log_interval=log_interval,
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)
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if rollout.continue_training is False:
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break
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self._update_current_progress_remaining(self.num_timesteps, total_timesteps)
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if self.num_timesteps > 0 and self.num_timesteps > self.learning_starts:
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if self.use_sde:
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if self.sde_log_std_scheduler is not None:
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# Call the scheduler
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value = self.sde_log_std_scheduler(self._current_progress_remaining)
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self.actor.log_std.data = th.ones_like(self.actor.log_std) * value
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else:
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# On-policy gradient
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self.train_sde()
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gradient_steps = self.gradient_steps if self.gradient_steps > 0 else rollout.episode_timesteps
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self.train(gradient_steps, batch_size=self.batch_size)
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callback.on_training_end()
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return self
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def collect_rollouts( # noqa: C901
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self,
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env: VecEnv,
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# Type hint as string to avoid circular import
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callback: "BaseCallback",
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n_episodes: int = 1,
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n_steps: int = -1,
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action_noise: Optional[ActionNoise] = None,
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learning_starts: int = 0,
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replay_buffer: Optional[ReplayBuffer] = None,
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log_interval: Optional[int] = None,
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) -> RolloutReturn:
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"""
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Collect rollout using the current policy (and possibly fill the replay buffer)
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:param env: (VecEnv) The training environment
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:param n_episodes: (int) Number of episodes to use to collect rollout data
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You can also specify a ``n_steps`` instead
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:param n_steps: (int) Number of steps to use to collect rollout data
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You can also specify a ``n_episodes`` instead.
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:param action_noise: (Optional[ActionNoise]) Action noise that will be used for exploration
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Required for deterministic policy (e.g. TD3). This can also be used
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in addition to the stochastic policy for SAC.
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:param callback: (BaseCallback) Callback that will be called at each step
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(and at the beginning and end of the rollout)
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:param learning_starts: (int) Number of steps before learning for the warm-up phase.
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:param replay_buffer: (ReplayBuffer)
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:param log_interval: (int) Log data every ``log_interval`` episodes
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:return: (RolloutReturn)
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"""
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episode_rewards, total_timesteps = [], []
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total_steps, total_episodes = 0, 0
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assert isinstance(env, VecEnv), "You must pass a VecEnv"
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assert env.num_envs == 1, "OffPolicyRLModel only support single environment"
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self.rollout_data = None
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if self.use_sde:
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self.actor.reset_noise()
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# Reset rollout data
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if self.on_policy_exploration:
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self.rollout_data = {key: [] for key in ["observations", "actions", "rewards", "dones", "values"]}
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callback.on_rollout_start()
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continue_training = True
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while total_steps < n_steps or total_episodes < n_episodes:
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done = False
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episode_reward, episode_timesteps = 0.0, 0
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while not done:
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if self.use_sde and self.sde_sample_freq > 0 and total_steps % self.sde_sample_freq == 0:
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# Sample a new noise matrix
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self.actor.reset_noise()
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# Select action randomly or according to policy
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if self.num_timesteps < learning_starts and not (self.use_sde and self.use_sde_at_warmup):
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# Warmup phase
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unscaled_action = np.array([self.action_space.sample()])
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else:
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# Note: we assume that the policy uses tanh to scale the action
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# We use non-deterministic action in the case of SAC, for TD3, it does not matter
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unscaled_action, _ = self.predict(self._last_obs, deterministic=False)
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# Rescale the action from [low, high] to [-1, 1]
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scaled_action = self.policy.scale_action(unscaled_action)
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if self.use_sde:
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# When using SDE, the action can be out of bounds
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# TODO: fix with squashing and account for that in the proba distribution
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clipped_action = np.clip(scaled_action, -1, 1)
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else:
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clipped_action = scaled_action
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# Add noise to the action (improve exploration)
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if action_noise is not None:
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# NOTE: in the original implementation of TD3, the noise was applied to the unscaled action
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# Update(October 2019): Not anymore
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clipped_action = np.clip(clipped_action + action_noise(), -1, 1)
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# Rescale and perform action
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new_obs, reward, done, infos = env.step(self.policy.unscale_action(clipped_action))
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# Only stop training if return value is False, not when it is None.
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if callback.on_step() is False:
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return RolloutReturn(0.0, total_steps, total_episodes, continue_training=False)
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episode_reward += reward
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# Retrieve reward and episode length if using Monitor wrapper
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self._update_info_buffer(infos, done)
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# Store data in replay buffer
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if replay_buffer is not None:
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# Store only the unnormalized version
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if self._vec_normalize_env is not None:
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new_obs_ = self._vec_normalize_env.get_original_obs()
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reward_ = self._vec_normalize_env.get_original_reward()
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else:
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# Avoid changing the original ones
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self._last_original_obs, new_obs_, reward_ = self._last_obs, new_obs, reward
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replay_buffer.add(self._last_original_obs, new_obs_, clipped_action, reward_, done)
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if self.rollout_data is not None:
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# Assume only one env
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self.rollout_data["observations"].append(self._last_obs[0].copy())
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self.rollout_data["actions"].append(scaled_action[0].copy())
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self.rollout_data["rewards"].append(reward[0].copy())
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self.rollout_data["dones"].append(done[0].copy())
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obs_tensor = th.FloatTensor(self._last_obs).to(self.device)
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self.rollout_data["values"].append(self.vf_net(obs_tensor)[0].cpu().detach().numpy())
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self._last_obs = new_obs
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# Save the unnormalized observation
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if self._vec_normalize_env is not None:
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self._last_original_obs = new_obs_
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self.num_timesteps += 1
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episode_timesteps += 1
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total_steps += 1
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if 0 < n_steps <= total_steps:
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break
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if done:
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total_episodes += 1
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self._episode_num += 1
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episode_rewards.append(episode_reward)
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total_timesteps.append(episode_timesteps)
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if action_noise is not None:
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action_noise.reset()
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# Log training infos
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if log_interval is not None and self._episode_num % log_interval == 0:
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fps = int(self.num_timesteps / (time.time() - self.start_time))
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logger.record("time/episodes", self._episode_num, exclude="tensorboard")
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if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0:
|
|
logger.record("rollout/ep_rew_mean", safe_mean([ep_info["r"] for ep_info in self.ep_info_buffer]))
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logger.record("rollout/ep_len_mean", safe_mean([ep_info["l"] for ep_info in self.ep_info_buffer]))
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|
logger.record("time/fps", fps)
|
|
logger.record("time/time_elapsed", int(time.time() - self.start_time), exclude="tensorboard")
|
|
logger.record("time/total timesteps", self.num_timesteps, exclude="tensorboard")
|
|
if self.use_sde:
|
|
logger.record("train/std", (self.actor.get_std()).mean().item())
|
|
|
|
if len(self.ep_success_buffer) > 0:
|
|
logger.record("rollout/success rate", safe_mean(self.ep_success_buffer))
|
|
# Pass the number of timesteps for tensorboard
|
|
logger.dump(step=self.num_timesteps)
|
|
|
|
mean_reward = np.mean(episode_rewards) if total_episodes > 0 else 0.0
|
|
|
|
# Post processing
|
|
if self.rollout_data is not None:
|
|
for key in ["observations", "actions", "rewards", "dones", "values"]:
|
|
self.rollout_data[key] = th.FloatTensor(np.array(self.rollout_data[key])).to(self.device)
|
|
|
|
self.rollout_data["returns"] = self.rollout_data["rewards"].clone() # pytype: disable=attribute-error
|
|
self.rollout_data["advantage"] = self.rollout_data["rewards"].clone() # pytype: disable=attribute-error
|
|
|
|
# Compute return and advantage
|
|
last_return = 0.0
|
|
for step in reversed(range(len(self.rollout_data["rewards"]))):
|
|
if step == len(self.rollout_data["rewards"]) - 1:
|
|
next_non_terminal = 1.0 - done[0]
|
|
next_value = self.vf_net(th.FloatTensor(self._last_obs).to(self.device))[0].detach()
|
|
last_return = self.rollout_data["rewards"][step] + next_non_terminal * next_value
|
|
else:
|
|
next_non_terminal = 1.0 - self.rollout_data["dones"][step + 1]
|
|
last_return = self.rollout_data["rewards"][step] + self.gamma * last_return * next_non_terminal
|
|
self.rollout_data["returns"][step] = last_return
|
|
self.rollout_data["advantage"] = self.rollout_data["returns"] - self.rollout_data["values"]
|
|
|
|
callback.on_rollout_end()
|
|
|
|
return RolloutReturn(mean_reward, total_steps, total_episodes, continue_training)
|
|
|
|
def excluded_save_params(self) -> List[str]:
|
|
"""
|
|
Returns the names of the parameters that should be excluded by default
|
|
when saving the model.
|
|
|
|
:return: (List[str]) List of parameters that should be excluded from save
|
|
"""
|
|
# Exclude aliases
|
|
return super(TD3, self).excluded_save_params() + ["actor", "critic", "vf_net", "actor_target", "critic_target"]
|
|
|
|
def get_torch_variables(self) -> Tuple[List[str], List[str]]:
|
|
"""
|
|
cf base class
|
|
"""
|
|
state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
|
|
return state_dicts, []
|