mirror of
https://github.com/saymrwulf/stable-baselines3.git
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308 lines
14 KiB
Python
308 lines
14 KiB
Python
from typing import List, Tuple, Optional
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import torch as th
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import torch.nn.functional as F
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import numpy as np
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from torchy_baselines.common.base_class import OffPolicyRLModel
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from torchy_baselines.common.buffers import ReplayBuffer
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from torchy_baselines.common.type_aliases import ReplayBufferSamples
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from torchy_baselines.td3.policies import TD3Policy
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class TD3(OffPolicyRLModel):
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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: (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
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:param buffer_size: (int) size of the replay buffer
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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 and Actor networks)
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it can be a function of the current progress (from 1 to 0)
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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 learning_starts: (int) how many steps of the model to collect transitions for before learning starts
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:param gamma: (float) the discount factor
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:param batch_size: (int) Minibatch size for each gradient update
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:param train_freq: (int) Update the model every `train_freq` steps.
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:param gradient_steps: (int) How many gradient update after each step
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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`
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:param tau: (float) the soft update coefficient ("Polyak update" of the target networks, between 0 and 1)
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:param action_noise: (ActionNoise) the action noise type. Cf common.noise for the different action noise type.
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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 none, 1 training information, 2 tensorflow 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__(self, policy, env, buffer_size=int(1e6), learning_rate=1e-3,
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policy_delay=2, learning_starts=100, gamma=0.99, batch_size=100,
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train_freq=-1, gradient_steps=-1, n_episodes_rollout=1,
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tau=0.005, action_noise=None, target_policy_noise=0.2, target_noise_clip=0.5,
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use_sde=False, sde_sample_freq=-1, sde_max_grad_norm=1,
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sde_ent_coef=0.0, sde_log_std_scheduler=None, use_sde_at_warmup=False,
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tensorboard_log=None, create_eval_env=False, policy_kwargs=None, verbose=0,
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seed=0, device='auto', _init_setup_model=True):
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super(TD3, self).__init__(policy, env, TD3Policy, policy_kwargs, verbose, device,
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create_eval_env=create_eval_env, seed=seed,
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use_sde=use_sde, sde_sample_freq=sde_sample_freq,
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use_sde_at_warmup=use_sde_at_warmup)
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self.buffer_size = buffer_size
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self.learning_rate = learning_rate
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self.learning_starts = learning_starts
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self.train_freq = train_freq
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self.gradient_steps = gradient_steps
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self.n_episodes_rollout = n_episodes_rollout
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self.batch_size = batch_size
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self.tau = tau
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self.gamma = gamma
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self.action_noise = action_noise
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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):
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self._setup_learning_rate()
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obs_dim, action_dim = self.observation_space.shape[0], self.action_space.shape[0]
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self.set_random_seed(self.seed)
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self.replay_buffer = ReplayBuffer(self.buffer_size, obs_dim, action_dim, self.device)
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self.policy = self.policy_class(self.observation_space, self.action_space,
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self.learning_rate, use_sde=self.use_sde,
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device=self.device, **self.policy_kwargs)
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self.policy = self.policy.to(self.device)
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self._create_aliases()
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def _create_aliases(self):
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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_critic(self, gradient_steps: int = 1,
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batch_size: int = 100,
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replay_data: Optional[ReplayBufferSamples] = None,
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tau: float = 0.0):
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# Update optimizer learning rate
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self._update_learning_rate(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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if replay_data is None:
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obs, action, next_obs, done, reward = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
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else:
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obs, action, next_obs, done, reward = replay_data
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# Select action according to policy and add clipped noise
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noise = action.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_action = (self.actor_target(next_obs) + noise).clamp(-1, 1)
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# Compute the target Q value
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target_q1, target_q2 = self.critic_target(next_obs, next_action)
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target_q = th.min(target_q1, target_q2)
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target_q = reward + ((1 - done) * self.gamma * target_q).detach()
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# Get current Q estimates
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current_q1, current_q2 = self.critic(obs, action)
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# Compute critic loss
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critic_loss = F.mse_loss(current_q1, target_q) + F.mse_loss(current_q2, target_q)
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# Optimize the critic
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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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# Update the frozen target models
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# Note: by default, for TD3, this update is done in train_actor
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# however, for CEMRL it is done here
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if tau > 0:
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for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
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target_param.data.copy_(tau * param.data + (1 - tau) * target_param.data)
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def train_actor(self, gradient_steps: int = 1,
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batch_size: int = 100,
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tau_actor: float = 0.005,
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tau_critic: float = 0.005,
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replay_data: Optional[ReplayBufferSamples] = None):
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# Update optimizer learning rate
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self._update_learning_rate(self.actor.optimizer)
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for gradient_step in range(gradient_steps):
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# Sample replay buffer
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if replay_data is None:
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obs, _, next_obs, done, reward = self.replay_buffer.sample(batch_size, env=self._vec_normalize_env)
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else:
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obs, _, next_obs, done, reward = replay_data
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# Compute actor loss
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actor_loss = -self.critic.q1_forward(obs, self.actor(obs)).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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# Update the frozen target models
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if tau_critic > 0:
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for param, target_param in zip(self.critic.parameters(), self.critic_target.parameters()):
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target_param.data.copy_(tau_critic * param.data + (1 - tau_critic) * target_param.data)
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for param, target_param in zip(self.actor.parameters(), self.actor_target.parameters()):
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target_param.data.copy_(tau_actor * param.data + (1 - tau_actor) * target_param.data)
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def train(self, gradient_steps: int, batch_size: int = 100, policy_delay: int = 2):
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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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self.train_critic(replay_data=replay_data)
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# Delayed policy updates
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if gradient_step % policy_delay == 0:
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self.train_actor(replay_data=replay_data, tau_actor=self.tau, tau_critic=self.tau)
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def train_sde(self):
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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 = [self.rollout_data[key] for key in
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['observations', 'actions', 'advantage', 'returns']]
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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()
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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(self, total_timesteps, callback=None, log_interval=4,
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eval_env=None, eval_freq=-1, n_eval_episodes=5,
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tb_log_name="TD3", eval_log_path=None, reset_num_timesteps=True):
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episode_num, obs, callback = self._setup_learn(eval_env, callback, eval_freq,
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n_eval_episodes, eval_log_path, reset_num_timesteps)
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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(self.env, n_episodes=self.n_episodes_rollout,
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n_steps=self.train_freq, 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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obs=obs, episode_num=episode_num,
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log_interval=log_interval)
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# Unpack
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episode_reward, episode_timesteps, n_episodes, obs, continue_training = rollout
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if continue_training is False:
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break
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episode_num += n_episodes
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self._update_current_progress(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)
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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 episode_timesteps
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self.train(gradient_steps, batch_size=self.batch_size, policy_delay=self.policy_delay)
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callback.on_training_end()
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return self
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def excluded_save_params(self) -> List[str]:
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"""
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Returns the names of the parameters that should be excluded by default
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when saving the model.
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:return: (List[str]) List of parameters that should be excluded from save
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"""
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# Exclude aliases
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return super(TD3, self).excluded_save_params() + ["actor", "critic", "vf_net", "actor_target", "critic_target"]
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def get_torch_variables(self) -> Tuple[List[str], List[str]]:
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"""
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cf base class
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"""
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state_dicts = ["policy", "actor.optimizer", "critic.optimizer"]
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return state_dicts, []
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