266 lines
11 KiB
Python
266 lines
11 KiB
Python
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from stable_baselines3.common.on_policy_algorithm import *
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class BetterOnPolicyAlgorithm(OnPolicyAlgorithm):
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"""
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The base for On-Policy algorithms (ex: A2C/PPO).
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:param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
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:param env: The environment to learn from (if registered in Gym, can be str)
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:param learning_rate: The learning rate, it can be a function
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of the current progress remaining (from 1 to 0)
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:param n_steps: The number of steps to run for each environment per update
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(i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
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:param gamma: Discount factor
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:param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator.
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Equivalent to classic advantage when set to 1.
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:param ent_coef: Entropy coefficient for the loss calculation
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:param vf_coef: Value function coefficient for the loss calculation
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:param max_grad_norm: The maximum value for the gradient clipping
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:param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
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instead of action noise exploration (default: False)
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:param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
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Default: -1 (only sample at the beginning of the rollout)
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:param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
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the reported success rate, mean episode length, and mean reward over
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:param tensorboard_log: the log location for tensorboard (if None, no logging)
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:param monitor_wrapper: When creating an environment, whether to wrap it
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or not in a Monitor wrapper.
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:param policy_kwargs: additional arguments to be passed to the policy on creation
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:param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
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debug messages
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:param seed: Seed for the pseudo random generators
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:param 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: Whether or not to build the network at the creation of the instance
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:param supported_action_spaces: The action spaces supported by the algorithm.
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"""
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def __init__(
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self,
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policy: Union[str, Type[ActorCriticPolicy]],
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env: Union[GymEnv, str],
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learning_rate: Union[float, Schedule],
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n_steps: int,
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gamma: float,
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gae_lambda: float,
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ent_coef: float,
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vf_coef: float,
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max_grad_norm: float,
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use_sde: bool,
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sde_sample_freq: int,
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use_pca: bool,
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stats_window_size: int = 100,
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tensorboard_log: Optional[str] = None,
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monitor_wrapper: bool = True,
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policy_kwargs: Optional[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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supported_action_spaces: Optional[Tuple[spaces.Space, ...]] = None,
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):
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assert not (use_sde and use_pca)
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self.use_pca = use_pca
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super().__init__(
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policy=policy,
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env=env,
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learning_rate=learning_rate,
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n_steps=n_steps,
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gamma=gamma,
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gae_lambda=gae_lambda,
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ent_coef=ent_coef,
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vf_coef=vf_coef,
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max_grad_norm=max_grad_norm,
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policy_kwargs=policy_kwargs,
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verbose=verbose,
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device=device,
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use_sde=use_sde,
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sde_sample_freq=sde_sample_freq,
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# support_multi_env=True,
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seed=seed,
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stats_window_size=stats_window_size,
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tensorboard_log=tensorboard_log,
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supported_action_spaces=supported_action_spaces,
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monitor_wrapper=monitor_wrapper,
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_init_setup_model=_init_setup_model
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)
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def _setup_model(self) -> None:
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self._setup_lr_schedule()
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self.set_random_seed(self.seed)
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buffer_cls = DictRolloutBuffer if isinstance(self.observation_space, spaces.Dict) else RolloutBuffer
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self.rollout_buffer = buffer_cls(
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self.n_steps,
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self.observation_space,
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self.action_space,
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device=self.device,
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gamma=self.gamma,
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gae_lambda=self.gae_lambda,
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n_envs=self.n_envs,
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)
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self.policy = self.policy_class( # pytype:disable=not-instantiable
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self.observation_space,
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self.action_space,
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self.lr_schedule,
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use_sde=self.use_sde,
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use_pca=self.use_pca,
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**self.policy_kwargs # pytype:disable=not-instantiable
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)
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self.policy = self.policy.to(self.device)
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def collect_rollouts(
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self,
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env: VecEnv,
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callback: BaseCallback,
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rollout_buffer: RolloutBuffer,
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n_rollout_steps: int,
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) -> bool:
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"""
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Collect experiences using the current policy and fill a ``RolloutBuffer``.
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The term rollout here refers to the model-free notion and should not
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be used with the concept of rollout used in model-based RL or planning.
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:param env: The training environment
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:param callback: 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 rollout_buffer: Buffer to fill with rollouts
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:param n_rollout_steps: Number of experiences to collect per environment
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:return: True if function returned with at least `n_rollout_steps`
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collected, False if callback terminated rollout prematurely.
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"""
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assert self._last_obs is not None, "No previous observation was provided"
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# Switch to eval mode (this affects batch norm / dropout)
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self.policy.set_training_mode(False)
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n_steps = 0
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rollout_buffer.reset()
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# Sample new weights for the state dependent exploration
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if self.use_sde:
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self.policy.reset_noise(env.num_envs)
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callback.on_rollout_start()
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while n_steps < n_rollout_steps:
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if self.use_sde and self.sde_sample_freq > 0 and n_steps % self.sde_sample_freq == 0:
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# Sample a new noise matrix
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self.policy.reset_noise(env.num_envs)
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with th.no_grad():
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# Convert to pytorch tensor or to TensorDict
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obs_tensor = obs_as_tensor(self._last_obs, self.device)
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actions, values, log_probs = self.policy(obs_tensor)
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actions = actions.cpu().numpy()
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# Rescale and perform action
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clipped_actions = actions
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# Clip the actions to avoid out of bound error
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if isinstance(self.action_space, spaces.Box):
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clipped_actions = np.clip(actions, self.action_space.low, self.action_space.high)
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new_obs, rewards, dones, infos = env.step(clipped_actions)
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self.num_timesteps += env.num_envs
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# Give access to local variables
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callback.update_locals(locals())
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if callback.on_step() is False:
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return False
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self._update_info_buffer(infos)
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n_steps += 1
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if isinstance(self.action_space, spaces.Discrete):
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# Reshape in case of discrete action
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actions = actions.reshape(-1, 1)
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# Handle timeout by bootstraping with value function
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# see GitHub issue #633
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for idx, done in enumerate(dones):
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if (
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done
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and infos[idx].get("terminal_observation") is not None
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and infos[idx].get("TimeLimit.truncated", False)
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):
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terminal_obs = self.policy.obs_to_tensor(infos[idx]["terminal_observation"])[0]
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with th.no_grad():
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terminal_value = self.policy.predict_values(terminal_obs)[0]
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rewards[idx] += self.gamma * terminal_value
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rollout_buffer.add(self._last_obs, actions, rewards, self._last_episode_starts, values, log_probs)
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self._last_obs = new_obs
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self._last_episode_starts = dones
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with th.no_grad():
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# Compute value for the last timestep
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values = self.policy.predict_values(obs_as_tensor(new_obs, self.device))
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rollout_buffer.compute_returns_and_advantage(last_values=values, dones=dones)
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callback.on_rollout_end()
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return True
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def train(self) -> None:
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"""
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Consume current rollout data and update policy parameters.
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Implemented by individual algorithms.
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"""
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raise NotImplementedError
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def learn(
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self: SelfOnPolicyAlgorithm,
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total_timesteps: int,
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callback: MaybeCallback = None,
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log_interval: int = 1,
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tb_log_name: str = "OnPolicyAlgorithm",
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reset_num_timesteps: bool = True,
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progress_bar: bool = False,
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) -> SelfOnPolicyAlgorithm:
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iteration = 0
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total_timesteps, callback = self._setup_learn(
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total_timesteps,
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callback,
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reset_num_timesteps,
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tb_log_name,
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progress_bar,
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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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continue_training = self.collect_rollouts(self.env, callback, self.rollout_buffer, n_rollout_steps=self.n_steps)
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if continue_training is False:
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break
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iteration += 1
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self._update_current_progress_remaining(self.num_timesteps, total_timesteps)
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# Display training infos
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if log_interval is not None and iteration % log_interval == 0:
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time_elapsed = max((time.time_ns() - self.start_time) / 1e9, sys.float_info.epsilon)
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fps = int((self.num_timesteps - self._num_timesteps_at_start) / time_elapsed)
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self.logger.record("time/iterations", iteration, exclude="tensorboard")
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if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0:
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self.logger.record("rollout/ep_rew_mean", safe_mean([ep_info["r"] for ep_info in self.ep_info_buffer]))
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self.logger.record("rollout/ep_len_mean", safe_mean([ep_info["l"] for ep_info in self.ep_info_buffer]))
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self.logger.record("time/fps", fps)
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self.logger.record("time/time_elapsed", int(time_elapsed), exclude="tensorboard")
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self.logger.record("time/total_timesteps", self.num_timesteps, exclude="tensorboard")
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self.logger.dump(step=self.num_timesteps)
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self.train()
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callback.on_training_end()
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return self
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def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
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state_dicts = ["policy", "policy.optimizer"]
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return state_dicts, []
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