mirror of
https://github.com/saymrwulf/stable-baselines3.git
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Remove SDE support for TD3
This commit is contained in:
Antonin RAFFIN
parent
9a9870fa9f
commit
c20af230f7
6 changed files with 40 additions and 372 deletions
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@ -3,12 +3,13 @@
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Changelog
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==========
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Pre-Release 0.6.0a6 (WIP)
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Pre-Release 0.6.0a7 (WIP)
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------------------------------
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Breaking Changes:
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^^^^^^^^^^^^^^^^^
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- Remove State-Dependent Exploration (SDE) support for ``TD3``
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New Features:
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^^^^^^^^^^^^^
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@ -686,6 +686,7 @@ class OffPolicyRLModel(BaseRLModel):
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Default: -1 (only sample at the beginning of the rollout)
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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 sde_support: (bool) Whether the model support SDE or not
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"""
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def __init__(self,
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@ -705,7 +706,8 @@ class OffPolicyRLModel(BaseRLModel):
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seed: Optional[int] = None,
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use_sde: bool = False,
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sde_sample_freq: int = -1,
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use_sde_at_warmup: bool = False):
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use_sde_at_warmup: bool = False,
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sde_support: bool = True):
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super(OffPolicyRLModel, self).__init__(policy, env, policy_base, learning_rate,
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policy_kwargs, verbose,
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@ -717,11 +719,10 @@ class OffPolicyRLModel(BaseRLModel):
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self.actor = None
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self.replay_buffer = None # type: Optional[ReplayBuffer]
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# Update policy keyword arguments
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self.policy_kwargs['use_sde'] = self.use_sde
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if sde_support:
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self.policy_kwargs['use_sde'] = self.use_sde
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self.policy_kwargs['device'] = self.device
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# For SDE only
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self.rollout_data = None
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self.on_policy_exploration = False
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self.use_sde_at_warmup = use_sde_at_warmup
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def _setup_model(self):
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@ -786,12 +787,8 @@ class OffPolicyRLModel(BaseRLModel):
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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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@ -816,23 +813,25 @@ class OffPolicyRLModel(BaseRLModel):
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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 isinstance(self.action_space, gym.spaces.Box):
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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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# 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(scaled_action + action_noise(), -1, 1)
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# We store the scaled action in the buffer
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buffer_action = clipped_action
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action = self.policy.unscale_action(clipped_action)
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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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# Discrete case, no need to normalize or clip
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buffer_action = unscaled_action
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action = buffer_action
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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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new_obs, reward, done, infos = env.step(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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@ -853,16 +852,7 @@ class OffPolicyRLModel(BaseRLModel):
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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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replay_buffer.add(self._last_original_obs, new_obs_, buffer_action, reward_, done)
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self._last_obs = new_obs
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# Save the unnormalized observation
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@ -880,6 +870,7 @@ class OffPolicyRLModel(BaseRLModel):
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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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@ -890,7 +881,6 @@ class OffPolicyRLModel(BaseRLModel):
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if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0:
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logger.logkv('ep_rew_mean', self.safe_mean([ep_info['r'] for ep_info in self.ep_info_buffer]))
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logger.logkv('ep_len_mean', self.safe_mean([ep_info['l'] for ep_info in self.ep_info_buffer]))
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# logger.logkv("n_updates", n_updates)
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logger.logkv("fps", fps)
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logger.logkv('time_elapsed', int(time.time() - self.start_time))
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logger.logkv("total timesteps", self.num_timesteps)
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@ -903,27 +893,6 @@ class OffPolicyRLModel(BaseRLModel):
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mean_reward = np.mean(episode_rewards) if total_episodes > 0 else 0.0
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# Post processing
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if self.rollout_data is not None:
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for key in ['observations', 'actions', 'rewards', 'dones', 'values']:
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self.rollout_data[key] = th.FloatTensor(np.array(self.rollout_data[key])).to(self.device)
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self.rollout_data['returns'] = self.rollout_data['rewards'].clone()
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self.rollout_data['advantage'] = self.rollout_data['rewards'].clone()
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# Compute return and advantage
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last_return = 0.0
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for step in reversed(range(len(self.rollout_data['rewards']))):
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if step == len(self.rollout_data['rewards']) - 1:
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next_non_terminal = 1.0 - done[0]
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next_value = self.vf_net(th.FloatTensor(self._last_obs).to(self.device))[0].detach()
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last_return = self.rollout_data['rewards'][step] + next_non_terminal * next_value
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else:
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next_non_terminal = 1.0 - self.rollout_data['dones'][step + 1]
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last_return = self.rollout_data['rewards'][step] + self.gamma * last_return * next_non_terminal
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self.rollout_data['returns'][step] = last_return
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self.rollout_data['advantage'] = self.rollout_data['returns'] - self.rollout_data['values']
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callback.on_rollout_end()
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return RolloutReturn(mean_reward, total_steps, total_episodes, continue_training)
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@ -6,9 +6,7 @@ import torch.nn as nn
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from stable_baselines3.common.preprocessing import get_action_dim
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from stable_baselines3.common.policies import (BasePolicy, register_policy, create_mlp,
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create_sde_features_extractor, NatureCNN,
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BaseFeaturesExtractor, FlattenExtractor)
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from stable_baselines3.common.distributions import StateDependentNoiseDistribution
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NatureCNN, BaseFeaturesExtractor, FlattenExtractor)
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class Actor(BasePolicy):
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@ -22,18 +20,6 @@ class Actor(BasePolicy):
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(a CNN when using images, a nn.Flatten() layer otherwise)
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:param features_dim: (int) Number of features
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:param activation_fn: (Type[nn.Module]) Activation function
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:param use_sde: (bool) Whether to use State Dependent Exploration or not
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:param log_std_init: (float) Initial value for the log standard deviation
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:param clip_noise: (float) Clip the magnitude of the noise
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:param lr_sde: (float) Learning rate for the standard deviation of the noise
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:param full_std: (bool) Whether to use (n_features x n_actions) parameters
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for the std instead of only (n_features,) when using SDE.
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:param sde_net_arch: ([int]) Network architecture for extracting features
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when using SDE. If None, the latent features from the policy will be used.
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Pass an empty list to use the states as features.
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:param use_expln: (bool) Use ``expln()`` function instead of ``exp()`` when using SDE to ensure
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a positive standard deviation (cf paper). It allows to keep variance
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above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
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:param normalize_images: (bool) Whether to normalize images or not,
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dividing by 255.0 (True by default)
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:param device: (Union[th.device, str]) Device on which the code should run.
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@ -45,65 +31,25 @@ class Actor(BasePolicy):
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features_extractor: nn.Module,
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features_dim: int,
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activation_fn: Type[nn.Module] = nn.ReLU,
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use_sde: bool = False,
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log_std_init: float = -3,
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clip_noise: Optional[float] = None,
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lr_sde: float = 3e-4,
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full_std: bool = False,
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sde_net_arch: Optional[List[int]] = None,
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use_expln: bool = False,
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normalize_images: bool = True,
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device: Union[th.device, str] = 'auto'):
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super(Actor, self).__init__(observation_space, action_space,
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features_extractor=features_extractor,
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normalize_images=normalize_images,
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device=device,
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squash_output=not use_sde)
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squash_output=True)
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self.latent_pi, self.log_std = None, None
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self.weights_dist, self.exploration_mat = None, None
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self.use_sde, self.sde_optimizer = use_sde, None
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self.full_std = full_std
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self.sde_features_extractor = None
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self.features_extractor = features_extractor
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self.normalize_images = normalize_images
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self.net_arch = net_arch
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self.features_dim = features_dim
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self.activation_fn = activation_fn
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self.clip_noise = clip_noise
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self.lr_sde = lr_sde
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self.log_std_init = log_std_init
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self.sde_net_arch = sde_net_arch
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self.use_expln = use_expln
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self.full_std = full_std
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action_dim = get_action_dim(self.action_space)
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if use_sde:
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latent_pi_net = create_mlp(features_dim, -1, net_arch, activation_fn, squash_output=False)
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self.latent_pi = nn.Sequential(*latent_pi_net)
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latent_sde_dim = net_arch[-1]
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learn_features = sde_net_arch is not None
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# Separate feature extractor for SDE
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if sde_net_arch is not None:
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self.sde_features_extractor, latent_sde_dim = create_sde_features_extractor(features_dim, sde_net_arch,
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activation_fn)
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# Create state dependent noise matrix (SDE)
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self.action_dist = StateDependentNoiseDistribution(action_dim, full_std=full_std, use_expln=use_expln,
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squash_output=False, learn_features=learn_features)
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action_net, self.log_std = self.action_dist.proba_distribution_net(latent_dim=net_arch[-1],
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latent_sde_dim=latent_sde_dim,
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log_std_init=log_std_init)
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# Squash output
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self.mu = nn.Sequential(action_net, nn.Tanh())
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self.sde_optimizer = th.optim.Adam([self.log_std], lr=lr_sde)
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self.reset_noise()
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else:
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actor_net = create_mlp(features_dim, action_dim, net_arch, activation_fn, squash_output=True)
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self.mu = nn.Sequential(*actor_net)
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actor_net = create_mlp(features_dim, action_dim, net_arch, activation_fn, squash_output=True)
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# Deterministic action
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self.mu = nn.Sequential(*actor_net)
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def _get_data(self) -> Dict[str, Any]:
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data = super()._get_data()
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@ -112,73 +58,14 @@ class Actor(BasePolicy):
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net_arch=self.net_arch,
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features_dim=self.features_dim,
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activation_fn=self.activation_fn,
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use_sde=self.use_sde,
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log_std_init=self.log_std_init,
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clip_noise=self.clip_noise,
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lr_sde=self.lr_sde,
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full_std=self.full_std,
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sde_net_arch=self.sde_net_arch,
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use_expln=self.use_expln,
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features_extractor=self.features_extractor
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))
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return data
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def get_std(self) -> th.Tensor:
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"""
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Retrieve the standard deviation of the action distribution.
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Only useful when using SDE.
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It corresponds to ``th.exp(log_std)`` in the normal case,
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but is slightly different when using ``expln`` function
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(cf StateDependentNoiseDistribution doc).
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:return: (th.Tensor)
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"""
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return self.action_dist.get_std(self.log_std)
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def _get_latent(self, obs: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
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features = self.extract_features(obs)
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latent_pi = self.latent_pi(features)
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latent_sde = self.sde_features_extractor(features) if self.sde_features_extractor is not None else latent_pi
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return latent_pi, latent_sde
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def evaluate_actions(self, obs: th.Tensor, actions: th.Tensor) -> Tuple[th.Tensor, th.Tensor]:
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"""
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Evaluate actions according to the current policy,
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given the observations. Only useful when using SDE.
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:param obs: (th.Tensor)
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:param actions: (th.Tensor)
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:return: (th.Tensor, th.Tensor) log likelihood of taking those actions
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and entropy of the action distribution.
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"""
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latent_pi, latent_sde = self._get_latent(obs)
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mean_actions = self.mu(latent_pi)
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distribution = self.action_dist.proba_distribution(mean_actions, self.log_std, latent_sde)
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log_prob = distribution.log_prob(actions)
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return log_prob, distribution.entropy()
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def reset_noise(self) -> None:
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"""
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Sample new weights for the exploration matrix, when using SDE.
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"""
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self.action_dist.sample_weights(self.log_std)
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def forward(self, obs: th.Tensor, deterministic: bool = True) -> th.Tensor:
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if self.use_sde:
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latent_pi, latent_sde = self._get_latent(obs)
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if deterministic:
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return self.mu(latent_pi)
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noise = self.action_dist.get_noise(latent_sde)
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if self.clip_noise is not None:
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noise = th.clamp(noise, -self.clip_noise, self.clip_noise)
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# TODO: Replace with squashing -> need to account for that in the sde update
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# -> set squash_output=True in the action_dist?
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# NOTE: the clipping is done in the rollout for now
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return self.mu(latent_pi) + noise
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else:
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features = self.extract_features(obs)
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return self.mu(features)
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# assert deterministic, 'The TD3 actor only outputs deterministic actions'
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features = self.extract_features(obs)
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return self.mu(features)
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def _predict(self, observation: th.Tensor, deterministic: bool = False) -> th.Tensor:
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return self.forward(observation, deterministic=deterministic)
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@ -234,43 +121,6 @@ class Critic(BasePolicy):
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return self.q1_net(th.cat([features, actions], dim=1))
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class ValueFunction(BasePolicy):
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"""
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Value function for TD3 when doing on-policy exploration with SDE.
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:param observation_space: (gym.spaces.Space) Obervation space
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:param action_space: (gym.spaces.Space) Action space
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:param features_extractor: (nn.Module) Network to extract features
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(a CNN when using images, a nn.Flatten() layer otherwise)
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:param features_dim: (int) Number of features
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:param net_arch: (Optional[List[int]]) Network architecture
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:param activation_fn: (Type[nn.Module]) Activation function
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:param normalize_images: (bool) Whether to normalize images or not,
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dividing by 255.0 (True by default)
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"""
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def __init__(self, observation_space: gym.spaces.Space,
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action_space: gym.spaces.Space,
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features_extractor: nn.Module,
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features_dim: int,
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net_arch: Optional[List[int]] = None,
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activation_fn: Type[nn.Module] = nn.Tanh,
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normalize_images: bool = True):
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super(ValueFunction, self).__init__(observation_space, action_space,
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features_extractor=features_extractor,
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normalize_images=normalize_images)
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if net_arch is None:
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net_arch = [64, 64]
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vf_net = create_mlp(features_dim, 1, net_arch, activation_fn)
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self.vf_net = nn.Sequential(*vf_net)
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def forward(self, obs: th.Tensor) -> th.Tensor:
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with th.no_grad():
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features = self.extract_features(obs)
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return self.vf_net(features)
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class TD3Policy(BasePolicy):
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"""
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Policy class (with both actor and critic) for TD3.
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@ -281,14 +131,6 @@ class TD3Policy(BasePolicy):
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:param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
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||||
:param device: (Union[th.device, str]) Device on which the code should run.
|
||||
:param activation_fn: (Type[nn.Module]) Activation function
|
||||
:param use_sde: (bool) Whether to use State Dependent Exploration or not
|
||||
:param log_std_init: (float) Initial value for the log standard deviation
|
||||
:param sde_net_arch: ([int]) Network architecture for extracting features
|
||||
when using SDE. If None, the latent features from the policy will be used.
|
||||
Pass an empty list to use the states as features.
|
||||
:param use_expln: (bool) Use ``expln()`` function instead of ``exp()`` when using SDE to ensure
|
||||
a positive standard deviation (cf paper). It allows to keep variance
|
||||
above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
|
||||
:param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
|
||||
:param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments
|
||||
to pass to the feature extractor.
|
||||
|
|
@ -305,12 +147,6 @@ class TD3Policy(BasePolicy):
|
|||
net_arch: Optional[List[int]] = None,
|
||||
device: Union[th.device, str] = 'auto',
|
||||
activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
use_sde: bool = False,
|
||||
log_std_init: float = -3,
|
||||
clip_noise: Optional[float] = None,
|
||||
lr_sde: float = 3e-4,
|
||||
sde_net_arch: Optional[List[int]] = None,
|
||||
use_expln: bool = False,
|
||||
features_extractor_class: Type[BaseFeaturesExtractor] = FlattenExtractor,
|
||||
features_extractor_kwargs: Optional[Dict[str, Any]] = None,
|
||||
normalize_images: bool = True,
|
||||
|
|
@ -347,23 +183,9 @@ class TD3Policy(BasePolicy):
|
|||
'normalize_images': normalize_images,
|
||||
'device': device
|
||||
}
|
||||
self.actor_kwargs = self.net_args.copy()
|
||||
sde_kwargs = {
|
||||
'use_sde': use_sde,
|
||||
'log_std_init': log_std_init,
|
||||
'clip_noise': clip_noise,
|
||||
'lr_sde': lr_sde,
|
||||
'sde_net_arch': sde_net_arch,
|
||||
'use_expln': use_expln
|
||||
}
|
||||
self.actor_kwargs.update(sde_kwargs)
|
||||
|
||||
self.actor, self.actor_target = None, None
|
||||
self.critic, self.critic_target = None, None
|
||||
# For SDE only
|
||||
self.use_sde = use_sde
|
||||
self.vf_net = None
|
||||
self.log_std_init = log_std_init
|
||||
|
||||
self._build(lr_schedule)
|
||||
|
||||
def _build(self, lr_schedule: Callable) -> None:
|
||||
|
|
@ -377,11 +199,6 @@ class TD3Policy(BasePolicy):
|
|||
self.critic_target.load_state_dict(self.critic.state_dict())
|
||||
self.critic.optimizer = self.optimizer_class(self.critic.parameters(), lr=lr_schedule(1),
|
||||
**self.optimizer_kwargs)
|
||||
if self.use_sde:
|
||||
self.vf_net = ValueFunction(self.observation_space, self.action_space,
|
||||
features_extractor=self.features_extractor,
|
||||
features_dim=self.features_dim)
|
||||
self.actor.sde_optimizer.add_param_group({'params': self.vf_net.parameters()}) # pytype: disable=attribute-error
|
||||
|
||||
def _get_data(self) -> Dict[str, Any]:
|
||||
data = super()._get_data()
|
||||
|
|
@ -389,12 +206,6 @@ class TD3Policy(BasePolicy):
|
|||
data.update(dict(
|
||||
net_arch=self.net_args['net_arch'],
|
||||
activation_fn=self.net_args['activation_fn'],
|
||||
use_sde=self.actor_kwargs['use_sde'],
|
||||
log_std_init=self.actor_kwargs['log_std_init'],
|
||||
clip_noise=self.actor_kwargs['clip_noise'],
|
||||
lr_sde=self.actor_kwargs['lr_sde'],
|
||||
sde_net_arch=self.actor_kwargs['sde_net_arch'],
|
||||
use_expln=self.actor_kwargs['use_expln'],
|
||||
lr_schedule=self._dummy_schedule, # dummy lr schedule, not needed for loading policy alone
|
||||
optimizer_class=self.optimizer_class,
|
||||
optimizer_kwargs=self.optimizer_kwargs,
|
||||
|
|
@ -403,11 +214,8 @@ class TD3Policy(BasePolicy):
|
|||
))
|
||||
return data
|
||||
|
||||
def reset_noise(self) -> None:
|
||||
return self.actor.reset_noise()
|
||||
|
||||
def make_actor(self) -> Actor:
|
||||
return Actor(**self.actor_kwargs).to(self.device)
|
||||
return Actor(**self.net_args).to(self.device)
|
||||
|
||||
def make_critic(self) -> Critic:
|
||||
return Critic(**self.net_args).to(self.device)
|
||||
|
|
@ -432,14 +240,6 @@ class CnnPolicy(TD3Policy):
|
|||
:param net_arch: (Optional[List[int]]) The specification of the policy and value networks.
|
||||
:param device: (Union[th.device, str]) Device on which the code should run.
|
||||
:param activation_fn: (Type[nn.Module]) Activation function
|
||||
:param use_sde: (bool) Whether to use State Dependent Exploration or not
|
||||
:param log_std_init: (float) Initial value for the log standard deviation
|
||||
:param sde_net_arch: ([int]) Network architecture for extracting features
|
||||
when using SDE. If None, the latent features from the policy will be used.
|
||||
Pass an empty list to use the states as features.
|
||||
:param use_expln: (bool) Use ``expln()`` function instead of ``exp()`` when using SDE to ensure
|
||||
a positive standard deviation (cf paper). It allows to keep variance
|
||||
above zero and prevent it from growing too fast. In practice, ``exp()`` is usually enough.
|
||||
:param features_extractor_class: (Type[BaseFeaturesExtractor]) Features extractor to use.
|
||||
:param features_extractor_kwargs: (Optional[Dict[str, Any]]) Keyword arguments
|
||||
to pass to the feature extractor.
|
||||
|
|
@ -456,12 +256,6 @@ class CnnPolicy(TD3Policy):
|
|||
net_arch: Optional[List[int]] = None,
|
||||
device: Union[th.device, str] = 'auto',
|
||||
activation_fn: Type[nn.Module] = nn.ReLU,
|
||||
use_sde: bool = False,
|
||||
log_std_init: float = -3,
|
||||
clip_noise: Optional[float] = None,
|
||||
lr_sde: float = 3e-4,
|
||||
sde_net_arch: Optional[List[int]] = None,
|
||||
use_expln: bool = False,
|
||||
features_extractor_class: Type[BaseFeaturesExtractor] = NatureCNN,
|
||||
features_extractor_kwargs: Optional[Dict[str, Any]] = None,
|
||||
normalize_images: bool = True,
|
||||
|
|
@ -473,12 +267,6 @@ class CnnPolicy(TD3Policy):
|
|||
net_arch,
|
||||
device,
|
||||
activation_fn,
|
||||
use_sde,
|
||||
log_std_init,
|
||||
clip_noise,
|
||||
lr_sde,
|
||||
sde_net_arch,
|
||||
use_expln,
|
||||
features_extractor_class,
|
||||
features_extractor_kwargs,
|
||||
normalize_images,
|
||||
|
|
|
|||
|
|
@ -39,15 +39,6 @@ class TD3(OffPolicyRLModel):
|
|||
:param target_policy_noise: (float) Standard deviation of Gaussian noise added to target policy
|
||||
(smoothing noise)
|
||||
:param target_noise_clip: (float) Limit for absolute value of target policy smoothing noise.
|
||||
:param use_sde: (bool) Whether to use State Dependent Exploration (SDE)
|
||||
instead of action noise exploration (default: False)
|
||||
:param sde_sample_freq: (int) Sample a new noise matrix every n steps when using SDE
|
||||
Default: -1 (only sample at the beginning of the rollout)
|
||||
:param sde_max_grad_norm: (float)
|
||||
:param sde_ent_coef: (float)
|
||||
:param sde_log_std_scheduler: (callable)
|
||||
:param use_sde_at_warmup: (bool) Whether to use SDE instead of uniform sampling
|
||||
during the warm up phase (before learning starts)
|
||||
:param create_eval_env: (bool) Whether to create a second environment that will be
|
||||
used for evaluating the agent periodically. (Only available when passing string for the environment)
|
||||
:param policy_kwargs: (dict) additional arguments to be passed to the policy on creation
|
||||
|
|
@ -73,12 +64,6 @@ class TD3(OffPolicyRLModel):
|
|||
policy_delay: int = 2,
|
||||
target_policy_noise: float = 0.2,
|
||||
target_noise_clip: float = 0.5,
|
||||
use_sde: bool = False,
|
||||
sde_sample_freq: int = -1,
|
||||
sde_max_grad_norm: float = 1,
|
||||
sde_ent_coef: float = 0.0,
|
||||
sde_log_std_scheduler: Optional[Callable] = None,
|
||||
use_sde_at_warmup: bool = False,
|
||||
tensorboard_log: Optional[str] = None,
|
||||
create_eval_env: bool = False,
|
||||
policy_kwargs: Dict[str, Any] = None,
|
||||
|
|
@ -91,8 +76,7 @@ class TD3(OffPolicyRLModel):
|
|||
buffer_size, learning_starts, batch_size,
|
||||
policy_kwargs, verbose, device,
|
||||
create_eval_env=create_eval_env, seed=seed,
|
||||
use_sde=use_sde, sde_sample_freq=sde_sample_freq,
|
||||
use_sde_at_warmup=use_sde_at_warmup)
|
||||
sde_support=False)
|
||||
|
||||
self.train_freq = train_freq
|
||||
self.gradient_steps = gradient_steps
|
||||
|
|
@ -104,13 +88,6 @@ class TD3(OffPolicyRLModel):
|
|||
self.target_noise_clip = target_noise_clip
|
||||
self.target_policy_noise = target_policy_noise
|
||||
|
||||
# State Dependent Exploration
|
||||
self.sde_max_grad_norm = sde_max_grad_norm
|
||||
self.sde_ent_coef = sde_ent_coef
|
||||
self.sde_log_std_scheduler = sde_log_std_scheduler
|
||||
self.on_policy_exploration = True
|
||||
self.sde_vf = None
|
||||
|
||||
if _init_setup_model:
|
||||
self._setup_model()
|
||||
|
||||
|
|
@ -123,7 +100,6 @@ class TD3(OffPolicyRLModel):
|
|||
self.actor_target = self.policy.actor_target
|
||||
self.critic = self.policy.critic
|
||||
self.critic_target = self.policy.critic_target
|
||||
self.vf_net = self.policy.vf_net
|
||||
|
||||
def train(self, gradient_steps: int, batch_size: int = 100, policy_delay: int = 2) -> None:
|
||||
|
||||
|
|
@ -178,52 +154,6 @@ class TD3(OffPolicyRLModel):
|
|||
self._n_updates += gradient_steps
|
||||
logger.logkv("n_updates", self._n_updates)
|
||||
|
||||
def train_sde(self) -> None:
|
||||
# Update optimizer learning rate
|
||||
# self._update_learning_rate(self.policy.optimizer)
|
||||
|
||||
# Unpack
|
||||
obs, action, advantage, returns = [self.rollout_data[key] for key in
|
||||
['observations', 'actions', 'advantage', 'returns']]
|
||||
|
||||
log_prob, entropy = self.actor.evaluate_actions(obs, action)
|
||||
values = self.vf_net(obs).flatten()
|
||||
|
||||
# Normalize advantage
|
||||
# if self.normalize_advantage:
|
||||
# advantage = (advantage - advantage.mean()) / (advantage.std() + 1e-8)
|
||||
|
||||
# Value loss using the TD(gae_lambda) target
|
||||
value_loss = F.mse_loss(returns, values)
|
||||
|
||||
# A2C loss
|
||||
policy_loss = -(advantage * log_prob).mean()
|
||||
|
||||
# Entropy loss favor exploration
|
||||
if entropy is None:
|
||||
# Approximate entropy when no analytical form
|
||||
entropy_loss = -log_prob.mean()
|
||||
else:
|
||||
entropy_loss = -th.mean(entropy)
|
||||
|
||||
vf_coef = 0.5
|
||||
loss = policy_loss + self.sde_ent_coef * entropy_loss + vf_coef * value_loss
|
||||
|
||||
# Optimization step
|
||||
self.actor.sde_optimizer.zero_grad()
|
||||
loss.backward()
|
||||
|
||||
assert not th.isnan(log_prob).any(), log_prob
|
||||
assert not th.isnan(entropy).any()
|
||||
assert not th.isnan(self.actor.log_std.grad).any()
|
||||
assert not th.isnan(self.actor.log_std).any()
|
||||
|
||||
# Clip grad norm
|
||||
th.nn.utils.clip_grad_norm_([self.actor.log_std], self.sde_max_grad_norm)
|
||||
self.actor.sde_optimizer.step()
|
||||
|
||||
del self.rollout_data
|
||||
|
||||
def learn(self,
|
||||
total_timesteps: int,
|
||||
callback: MaybeCallback = None,
|
||||
|
|
@ -255,16 +185,6 @@ class TD3(OffPolicyRLModel):
|
|||
self._update_current_progress(self.num_timesteps, total_timesteps)
|
||||
|
||||
if self.num_timesteps > 0 and self.num_timesteps > self.learning_starts:
|
||||
|
||||
if self.use_sde:
|
||||
if self.sde_log_std_scheduler is not None:
|
||||
# Call the scheduler
|
||||
value = self.sde_log_std_scheduler(self._current_progress)
|
||||
self.actor.log_std.data = th.ones_like(self.actor.log_std) * value
|
||||
else:
|
||||
# On-policy gradient
|
||||
self.train_sde()
|
||||
|
||||
gradient_steps = self.gradient_steps if self.gradient_steps > 0 else rollout.episode_timesteps
|
||||
self.train(gradient_steps, batch_size=self.batch_size, policy_delay=self.policy_delay)
|
||||
|
||||
|
|
@ -280,7 +200,7 @@ class TD3(OffPolicyRLModel):
|
|||
: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"]
|
||||
return super(TD3, self).excluded_save_params() + ["actor", "critic", "actor_target", "critic_target"]
|
||||
|
||||
def get_torch_variables(self) -> Tuple[List[str], List[str]]:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
0.6.0a6
|
||||
0.6.0a7
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@ import pytest
|
|||
import torch as th
|
||||
from torch.distributions import Normal
|
||||
|
||||
from stable_baselines3 import A2C, TD3, SAC, PPO
|
||||
from stable_baselines3 import A2C, SAC, PPO
|
||||
|
||||
|
||||
def test_state_dependent_exploration_grad():
|
||||
|
|
@ -54,20 +54,10 @@ def test_state_dependent_exploration_grad():
|
|||
assert sigma_hat.grad.allclose(grad)
|
||||
|
||||
|
||||
@pytest.mark.parametrize("model_class", [TD3, SAC, A2C, PPO])
|
||||
@pytest.mark.parametrize("model_class", [SAC, A2C, PPO])
|
||||
@pytest.mark.parametrize("sde_net_arch", [None, [32, 16], []])
|
||||
@pytest.mark.parametrize("use_expln", [False, True])
|
||||
def test_state_dependent_offpolicy_noise(model_class, sde_net_arch, use_expln):
|
||||
model = model_class('MlpPolicy', 'Pendulum-v0', use_sde=True, seed=None, create_eval_env=True,
|
||||
verbose=1, policy_kwargs=dict(log_std_init=-2, sde_net_arch=sde_net_arch, use_expln=use_expln))
|
||||
model.learn(total_timesteps=int(500), eval_freq=250)
|
||||
|
||||
|
||||
def test_scheduler():
|
||||
def scheduler(progress):
|
||||
return -2.0 * progress + 1
|
||||
|
||||
model = TD3('MlpPolicy', 'Pendulum-v0', use_sde=True, seed=None, create_eval_env=True,
|
||||
verbose=1, sde_log_std_scheduler=scheduler)
|
||||
model.learn(total_timesteps=int(1000), eval_freq=500)
|
||||
assert th.isclose(model.actor.log_std, th.ones_like(model.actor.log_std)).all()
|
||||
|
|
|
|||
Loading…
Reference in a new issue