Implemented binding to PCA

sbBrix/common/distributions.py

@@ -0,0 +1,45 @@
+from stable_baselines3.common.distributions import *
+from priorConditionedAnnealing import PCA_Distribution
+
+
+def _patched_make_proba_distribution(
+    action_space: spaces.Space, use_sde: bool = False, use_pca: bool = False, dist_kwargs: Optional[Dict[str, Any]] = None
+) -> Distribution:
+    """
+    Return an instance of Distribution for the correct type of action space
+
+    :param action_space: the input action space
+    :param use_sde: Force the use of StateDependentNoiseDistribution
+        instead of DiagGaussianDistribution
+    :param dist_kwargs: Keyword arguments to pass to the probability distribution
+    :return: the appropriate Distribution object
+    """
+
+    assert not (use_sde and use_pca), 'Can not mix sde and pca!'
+
+    if dist_kwargs is None:
+        dist_kwargs = {}
+
+    if isinstance(action_space, spaces.Box):
+        if use_sde:
+            cls = StateDependentNoiseDistribution
+        elif use_pca:
+            cls = PCA_Distribution
+        else:
+            cls = DiagGaussianDistribution
+        return cls(get_action_dim(action_space), **dist_kwargs)
+    elif isinstance(action_space, spaces.Discrete):
+        return CategoricalDistribution(action_space.n, **dist_kwargs)
+    elif isinstance(action_space, spaces.MultiDiscrete):
+        return MultiCategoricalDistribution(list(action_space.nvec), **dist_kwargs)
+    elif isinstance(action_space, spaces.MultiBinary):
+        return BernoulliDistribution(action_space.n, **dist_kwargs)
+    else:
+        raise NotImplementedError(
+            "Error: probability distribution, not implemented for action space"
+            f"of type {type(action_space)}."
+            " Must be of type Gym Spaces: Box, Discrete, MultiDiscrete or MultiBinary."
+        )
+
+
+_orig_make_propa_distribution, make_proba_distribution = make_proba_distribution, _patched_make_proba_distribution

sbBrix/common/off_policy_algorithm.py

@@ -0,0 +1,554 @@
+from stable_baselines3.common.off_policy_algorithm import *
+
+
+class BetterOffPolicyAlgorithm(OffPolicyAlgorithm):
+    """
+    The base for Off-Policy algorithms (ex: SAC/TD3)
+
+    :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
+    :param env: The environment to learn from
+                (if registered in Gym, can be str. Can be None for loading trained models)
+    :param learning_rate: learning rate for the optimizer,
+        it can be a function of the current progress remaining (from 1 to 0)
+    :param buffer_size: size of the replay buffer
+    :param learning_starts: how many steps of the model to collect transitions for before learning starts
+    :param batch_size: Minibatch size for each gradient update
+    :param tau: the soft update coefficient ("Polyak update", between 0 and 1)
+    :param gamma: the discount factor
+    :param train_freq: Update the model every ``train_freq`` steps. Alternatively pass a tuple of frequency and unit
+        like ``(5, "step")`` or ``(2, "episode")``.
+    :param gradient_steps: How many gradient steps to do after each rollout (see ``train_freq``)
+        Set to ``-1`` means to do as many gradient steps as steps done in the environment
+        during the rollout.
+    :param action_noise: the action noise type (None by default), this can help
+        for hard exploration problem. Cf common.noise for the different action noise type.
+    :param replay_buffer_class: Replay buffer class to use (for instance ``HerReplayBuffer``).
+        If ``None``, it will be automatically selected.
+    :param replay_buffer_kwargs: Keyword arguments to pass to the replay buffer on creation.
+    :param optimize_memory_usage: Enable a memory efficient variant of the replay buffer
+        at a cost of more complexity.
+        See https://github.com/DLR-RM/stable-baselines3/issues/37#issuecomment-637501195
+    :param policy_kwargs: Additional arguments to be passed to the policy on creation
+    :param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
+        the reported success rate, mean episode length, and mean reward over
+    :param tensorboard_log: the log location for tensorboard (if None, no logging)
+    :param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
+        debug messages
+    :param device: Device on which the code should run.
+        By default, it will try to use a Cuda compatible device and fallback to cpu
+        if it is not possible.
+    :param support_multi_env: Whether the algorithm supports training
+        with multiple environments (as in A2C)
+    :param monitor_wrapper: When creating an environment, whether to wrap it
+        or not in a Monitor wrapper.
+    :param seed: Seed for the pseudo random generators
+    :param use_sde: Whether to use State Dependent Exploration (SDE)
+        instead of action noise exploration (default: False)
+    :param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
+        Default: -1 (only sample at the beginning of the rollout)
+    :param use_sde_at_warmup: Whether to use gSDE instead of uniform sampling
+        during the warm up phase (before learning starts)
+    :param sde_support: Whether the model support gSDE or not
+    :param supported_action_spaces: The action spaces supported by the algorithm.
+    """
+
+    def __init__(
+        self,
+        policy: Union[str, Type[BasePolicy]],
+        env: Union[GymEnv, str],
+        learning_rate: Union[float, Schedule],
+        buffer_size: int = 1_000_000,  # 1e6
+        learning_starts: int = 100,
+        batch_size: int = 256,
+        tau: float = 0.005,
+        gamma: float = 0.99,
+        train_freq: Union[int, Tuple[int, str]] = (1, "step"),
+        gradient_steps: int = 1,
+        action_noise: Optional[ActionNoise] = None,
+        replay_buffer_class: Optional[Type[ReplayBuffer]] = None,
+        replay_buffer_kwargs: Optional[Dict[str, Any]] = None,
+        optimize_memory_usage: bool = False,
+        policy_kwargs: Optional[Dict[str, Any]] = None,
+        stats_window_size: int = 100,
+        tensorboard_log: Optional[str] = None,
+        verbose: int = 0,
+        device: Union[th.device, str] = "auto",
+        support_multi_env: bool = False,
+        monitor_wrapper: bool = True,
+        seed: Optional[int] = None,
+        use_sde: bool = False,
+        sde_sample_freq: int = -1,
+        use_sde_at_warmup: bool = False,
+        sde_support: bool = True,
+        use_pca: bool = False,
+        supported_action_spaces: Optional[Tuple[spaces.Space, ...]] = None,
+    ):
+        assert not (use_sde and use_pca)
+        self.use_pca = use_pca
+        super().__init__(
+            policy=policy,
+            env=env,
+            learning_rate=learning_rate,
+            policy_kwargs=policy_kwargs,
+            stats_window_size=stats_window_size,
+            tensorboard_log=tensorboard_log,
+            verbose=verbose,
+            device=device,
+            support_multi_env=support_multi_env,
+            monitor_wrapper=monitor_wrapper,
+            seed=seed,
+            use_sde=use_sde,
+            sde_sample_freq=sde_sample_freq,
+            supported_action_spaces=supported_action_spaces,
+            buffer_size=buffer_size,
+            batch_size=batch_size,
+            learning_starts=learning_starts,
+            tau=tau,
+            gamma=gamma,
+            gradient_steps=gradient_steps,
+            action_noise=action_noise,
+            optimize_memory_usage=optimize_memory_usage,
+            replay_buffer_class=replay_buffer_class,
+            replay_buffer_kwargs=replay_buffer_kwargs,
+            train_freq=train_freq,
+            use_sde_at_warmup=use_sde_at_warmup,
+            sde_support=sde_support,
+        )
+
+    def _convert_train_freq(self) -> None:
+        """
+        Convert `train_freq` parameter (int or tuple)
+        to a TrainFreq object.
+        """
+        if not isinstance(self.train_freq, TrainFreq):
+            train_freq = self.train_freq
+
+            # The value of the train frequency will be checked later
+            if not isinstance(train_freq, tuple):
+                train_freq = (train_freq, "step")
+
+            try:
+                train_freq = (train_freq[0], TrainFrequencyUnit(train_freq[1]))
+            except ValueError as e:
+                raise ValueError(
+                    f"The unit of the `train_freq` must be either 'step' or 'episode' not '{train_freq[1]}'!"
+                ) from e
+
+            if not isinstance(train_freq[0], int):
+                raise ValueError(f"The frequency of `train_freq` must be an integer and not {train_freq[0]}")
+
+            self.train_freq = TrainFreq(*train_freq)
+
+    def _setup_model(self) -> None:
+        self._setup_lr_schedule()
+        self.set_random_seed(self.seed)
+
+        if self.replay_buffer_class is None:
+            if isinstance(self.observation_space, spaces.Dict):
+                self.replay_buffer_class = DictReplayBuffer
+            else:
+                self.replay_buffer_class = ReplayBuffer
+
+        if self.replay_buffer is None:
+            # Make a local copy as we should not pickle
+            # the environment when using HerReplayBuffer
+            replay_buffer_kwargs = self.replay_buffer_kwargs.copy()
+            if issubclass(self.replay_buffer_class, HerReplayBuffer):
+                assert self.env is not None, "You must pass an environment when using `HerReplayBuffer`"
+                replay_buffer_kwargs["env"] = self.env
+            self.replay_buffer = self.replay_buffer_class(
+                self.buffer_size,
+                self.observation_space,
+                self.action_space,
+                device=self.device,
+                n_envs=self.n_envs,
+                optimize_memory_usage=self.optimize_memory_usage,
+                **replay_buffer_kwargs,  # pytype:disable=wrong-keyword-args
+            )
+
+        self.policy = self.policy_class(  # pytype:disable=not-instantiable
+            self.observation_space,
+            self.action_space,
+            self.lr_schedule,
+            **self.policy_kwargs,  # pytype:disable=not-instantiable
+        )
+        self.policy = self.policy.to(self.device)
+
+        # Convert train freq parameter to TrainFreq object
+        self._convert_train_freq()
+
+    def save_replay_buffer(self, path: Union[str, pathlib.Path, io.BufferedIOBase]) -> None:
+        """
+        Save the replay buffer as a pickle file.
+
+        :param path: Path to the file where the replay buffer should be saved.
+            if path is a str or pathlib.Path, the path is automatically created if necessary.
+        """
+        assert self.replay_buffer is not None, "The replay buffer is not defined"
+        save_to_pkl(path, self.replay_buffer, self.verbose)
+
+    def load_replay_buffer(
+        self,
+        path: Union[str, pathlib.Path, io.BufferedIOBase],
+        truncate_last_traj: bool = True,
+    ) -> None:
+        """
+        Load a replay buffer from a pickle file.
+
+        :param path: Path to the pickled replay buffer.
+        :param truncate_last_traj: When using ``HerReplayBuffer`` with online sampling:
+            If set to ``True``, we assume that the last trajectory in the replay buffer was finished
+            (and truncate it).
+            If set to ``False``, we assume that we continue the same trajectory (same episode).
+        """
+        self.replay_buffer = load_from_pkl(path, self.verbose)
+        assert isinstance(self.replay_buffer, ReplayBuffer), "The replay buffer must inherit from ReplayBuffer class"
+
+        # Backward compatibility with SB3 < 2.1.0 replay buffer
+        # Keep old behavior: do not handle timeout termination separately
+        if not hasattr(self.replay_buffer, "handle_timeout_termination"):  # pragma: no cover
+            self.replay_buffer.handle_timeout_termination = False
+            self.replay_buffer.timeouts = np.zeros_like(self.replay_buffer.dones)
+
+        if isinstance(self.replay_buffer, HerReplayBuffer):
+            assert self.env is not None, "You must pass an environment at load time when using `HerReplayBuffer`"
+            self.replay_buffer.set_env(self.get_env())
+            if truncate_last_traj:
+                self.replay_buffer.truncate_last_trajectory()
+
+    def _setup_learn(
+        self,
+        total_timesteps: int,
+        callback: MaybeCallback = None,
+        reset_num_timesteps: bool = True,
+        tb_log_name: str = "run",
+        progress_bar: bool = False,
+    ) -> Tuple[int, BaseCallback]:
+        """
+        cf `BaseAlgorithm`.
+        """
+        # Prevent continuity issue by truncating trajectory
+        # when using memory efficient replay buffer
+        # see https://github.com/DLR-RM/stable-baselines3/issues/46
+
+        replay_buffer = self.replay_buffer
+
+        truncate_last_traj = (
+            self.optimize_memory_usage
+            and reset_num_timesteps
+            and replay_buffer is not None
+            and (replay_buffer.full or replay_buffer.pos > 0)
+        )
+
+        if truncate_last_traj:
+            warnings.warn(
+                "The last trajectory in the replay buffer will be truncated, "
+                "see https://github.com/DLR-RM/stable-baselines3/issues/46."
+                "You should use `reset_num_timesteps=False` or `optimize_memory_usage=False`"
+                "to avoid that issue."
+            )
+            # Go to the previous index
+            pos = (replay_buffer.pos - 1) % replay_buffer.buffer_size
+            replay_buffer.dones[pos] = True
+
+        return super()._setup_learn(
+            total_timesteps,
+            callback,
+            reset_num_timesteps,
+            tb_log_name,
+            progress_bar,
+        )
+
+    def learn(
+        self: SelfOffPolicyAlgorithm,
+        total_timesteps: int,
+        callback: MaybeCallback = None,
+        log_interval: int = 4,
+        tb_log_name: str = "run",
+        reset_num_timesteps: bool = True,
+        progress_bar: bool = False,
+    ) -> SelfOffPolicyAlgorithm:
+        total_timesteps, callback = self._setup_learn(
+            total_timesteps,
+            callback,
+            reset_num_timesteps,
+            tb_log_name,
+            progress_bar,
+        )
+
+        callback.on_training_start(locals(), globals())
+
+        while self.num_timesteps < total_timesteps:
+            rollout = self.collect_rollouts(
+                self.env,
+                train_freq=self.train_freq,
+                action_noise=self.action_noise,
+                callback=callback,
+                learning_starts=self.learning_starts,
+                replay_buffer=self.replay_buffer,
+                log_interval=log_interval,
+            )
+
+            if rollout.continue_training is False:
+                break
+
+            if self.num_timesteps > 0 and self.num_timesteps > self.learning_starts:
+                # If no `gradient_steps` is specified,
+                # do as many gradients steps as steps performed during the rollout
+                gradient_steps = self.gradient_steps if self.gradient_steps >= 0 else rollout.episode_timesteps
+                # Special case when the user passes `gradient_steps=0`
+                if gradient_steps > 0:
+                    self.train(batch_size=self.batch_size, gradient_steps=gradient_steps)
+
+        callback.on_training_end()
+
+        return self
+
+    def train(self, gradient_steps: int, batch_size: int) -> None:
+        """
+        Sample the replay buffer and do the updates
+        (gradient descent and update target networks)
+        """
+        raise NotImplementedError()
+
+    def _sample_action(
+        self,
+        learning_starts: int,
+        action_noise: Optional[ActionNoise] = None,
+        n_envs: int = 1,
+    ) -> Tuple[np.ndarray, np.ndarray]:
+        """
+        Sample an action according to the exploration policy.
+        This is either done by sampling the probability distribution of the policy,
+        or sampling a random action (from a uniform distribution over the action space)
+        or by adding noise to the deterministic output.
+
+        :param action_noise: Action noise that will be used for exploration
+            Required for deterministic policy (e.g. TD3). This can also be used
+            in addition to the stochastic policy for SAC.
+        :param learning_starts: Number of steps before learning for the warm-up phase.
+        :param n_envs:
+        :return: action to take in the environment
+            and scaled action that will be stored in the replay buffer.
+            The two differs when the action space is not normalized (bounds are not [-1, 1]).
+        """
+        # Select action randomly or according to policy
+        if self.num_timesteps < learning_starts and not (self.use_sde and self.use_sde_at_warmup):
+            # Warmup phase
+            unscaled_action = np.array([self.action_space.sample() for _ in range(n_envs)])
+        else:
+            # Note: when using continuous actions,
+            # we assume that the policy uses tanh to scale the action
+            # We use non-deterministic action in the case of SAC, for TD3, it does not matter
+            unscaled_action, _ = self.predict(self._last_obs, deterministic=False)
+
+        # Rescale the action from [low, high] to [-1, 1]
+        if isinstance(self.action_space, spaces.Box):
+            scaled_action = self.policy.scale_action(unscaled_action)
+
+            # Add noise to the action (improve exploration)
+            if action_noise is not None:
+                scaled_action = np.clip(scaled_action + action_noise(), -1, 1)
+
+            # We store the scaled action in the buffer
+            buffer_action = scaled_action
+            action = self.policy.unscale_action(scaled_action)
+        else:
+            # Discrete case, no need to normalize or clip
+            buffer_action = unscaled_action
+            action = buffer_action
+        return action, buffer_action
+
+    def _dump_logs(self) -> None:
+        """
+        Write log.
+        """
+        time_elapsed = max((time.time_ns() - self.start_time) / 1e9, sys.float_info.epsilon)
+        fps = int((self.num_timesteps - self._num_timesteps_at_start) / time_elapsed)
+        self.logger.record("time/episodes", self._episode_num, exclude="tensorboard")
+        if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0:
+            self.logger.record("rollout/ep_rew_mean", safe_mean([ep_info["r"] for ep_info in self.ep_info_buffer]))
+            self.logger.record("rollout/ep_len_mean", safe_mean([ep_info["l"] for ep_info in self.ep_info_buffer]))
+        self.logger.record("time/fps", fps)
+        self.logger.record("time/time_elapsed", int(time_elapsed), exclude="tensorboard")
+        self.logger.record("time/total_timesteps", self.num_timesteps, exclude="tensorboard")
+        if self.use_sde or self.use_pca:
+            self.logger.record("train/std", (self.actor.get_std()).mean().item())
+
+        if len(self.ep_success_buffer) > 0:
+            self.logger.record("rollout/success_rate", safe_mean(self.ep_success_buffer))
+        # Pass the number of timesteps for tensorboard
+        self.logger.dump(step=self.num_timesteps)
+
+    def _on_step(self) -> None:
+        """
+        Method called after each step in the environment.
+        It is meant to trigger DQN target network update
+        but can be used for other purposes
+        """
+        pass
+
+    def _store_transition(
+        self,
+        replay_buffer: ReplayBuffer,
+        buffer_action: np.ndarray,
+        new_obs: Union[np.ndarray, Dict[str, np.ndarray]],
+        reward: np.ndarray,
+        dones: np.ndarray,
+        infos: List[Dict[str, Any]],
+    ) -> None:
+        """
+        Store transition in the replay buffer.
+        We store the normalized action and the unnormalized observation.
+        It also handles terminal observations (because VecEnv resets automatically).
+
+        :param replay_buffer: Replay buffer object where to store the transition.
+        :param buffer_action: normalized action
+        :param new_obs: next observation in the current episode
+            or first observation of the episode (when dones is True)
+        :param reward: reward for the current transition
+        :param dones: Termination signal
+        :param infos: List of additional information about the transition.
+            It may contain the terminal observations and information about timeout.
+        """
+        # Store only the unnormalized version
+        if self._vec_normalize_env is not None:
+            new_obs_ = self._vec_normalize_env.get_original_obs()
+            reward_ = self._vec_normalize_env.get_original_reward()
+        else:
+            # Avoid changing the original ones
+            self._last_original_obs, new_obs_, reward_ = self._last_obs, new_obs, reward
+
+        # Avoid modification by reference
+        next_obs = deepcopy(new_obs_)
+        # As the VecEnv resets automatically, new_obs is already the
+        # first observation of the next episode
+        for i, done in enumerate(dones):
+            if done and infos[i].get("terminal_observation") is not None:
+                if isinstance(next_obs, dict):
+                    next_obs_ = infos[i]["terminal_observation"]
+                    # VecNormalize normalizes the terminal observation
+                    if self._vec_normalize_env is not None:
+                        next_obs_ = self._vec_normalize_env.unnormalize_obs(next_obs_)
+                    # Replace next obs for the correct envs
+                    for key in next_obs.keys():
+                        next_obs[key][i] = next_obs_[key]
+                else:
+                    next_obs[i] = infos[i]["terminal_observation"]
+                    # VecNormalize normalizes the terminal observation
+                    if self._vec_normalize_env is not None:
+                        next_obs[i] = self._vec_normalize_env.unnormalize_obs(next_obs[i, :])
+
+        replay_buffer.add(
+            self._last_original_obs,
+            next_obs,
+            buffer_action,
+            reward_,
+            dones,
+            infos,
+        )
+
+        self._last_obs = new_obs
+        # Save the unnormalized observation
+        if self._vec_normalize_env is not None:
+            self._last_original_obs = new_obs_
+
+    def collect_rollouts(
+        self,
+        env: VecEnv,
+        callback: BaseCallback,
+        train_freq: TrainFreq,
+        replay_buffer: ReplayBuffer,
+        action_noise: Optional[ActionNoise] = None,
+        learning_starts: int = 0,
+        log_interval: Optional[int] = None,
+    ) -> RolloutReturn:
+        """
+        Collect experiences and store them into a ``ReplayBuffer``.
+
+        :param env: The training environment
+        :param callback: Callback that will be called at each step
+            (and at the beginning and end of the rollout)
+        :param train_freq: How much experience to collect
+            by doing rollouts of current policy.
+            Either ``TrainFreq(<n>, TrainFrequencyUnit.STEP)``
+            or ``TrainFreq(<n>, TrainFrequencyUnit.EPISODE)``
+            with ``<n>`` being an integer greater than 0.
+        :param action_noise: Action noise that will be used for exploration
+            Required for deterministic policy (e.g. TD3). This can also be used
+            in addition to the stochastic policy for SAC.
+        :param learning_starts: Number of steps before learning for the warm-up phase.
+        :param replay_buffer:
+        :param log_interval: Log data every ``log_interval`` episodes
+        :return:
+        """
+        # Switch to eval mode (this affects batch norm / dropout)
+        self.policy.set_training_mode(False)
+
+        num_collected_steps, num_collected_episodes = 0, 0
+
+        assert isinstance(env, VecEnv), "You must pass a VecEnv"
+        assert train_freq.frequency > 0, "Should at least collect one step or episode."
+
+        if env.num_envs > 1:
+            assert train_freq.unit == TrainFrequencyUnit.STEP, "You must use only one env when doing episodic training."
+
+        # Vectorize action noise if needed
+        if action_noise is not None and env.num_envs > 1 and not isinstance(action_noise, VectorizedActionNoise):
+            action_noise = VectorizedActionNoise(action_noise, env.num_envs)
+
+        if self.use_sde:
+            self.actor.reset_noise(env.num_envs)
+
+        callback.on_rollout_start()
+        continue_training = True
+        while should_collect_more_steps(train_freq, num_collected_steps, num_collected_episodes):
+            if self.use_sde and self.sde_sample_freq > 0 and num_collected_steps % self.sde_sample_freq == 0:
+                # Sample a new noise matrix
+                self.actor.reset_noise(env.num_envs)
+
+            # Select action randomly or according to policy
+            actions, buffer_actions = self._sample_action(learning_starts, action_noise, env.num_envs)
+
+            # Rescale and perform action
+            new_obs, rewards, dones, infos = env.step(actions)
+
+            self.num_timesteps += env.num_envs
+            num_collected_steps += 1
+
+            # Give access to local variables
+            callback.update_locals(locals())
+            # Only stop training if return value is False, not when it is None.
+            if callback.on_step() is False:
+                return RolloutReturn(num_collected_steps * env.num_envs, num_collected_episodes, continue_training=False)
+
+            # Retrieve reward and episode length if using Monitor wrapper
+            self._update_info_buffer(infos, dones)
+
+            # Store data in replay buffer (normalized action and unnormalized observation)
+            self._store_transition(replay_buffer, buffer_actions, new_obs, rewards, dones, infos)
+
+            self._update_current_progress_remaining(self.num_timesteps, self._total_timesteps)
+
+            # For DQN, check if the target network should be updated
+            # and update the exploration schedule
+            # For SAC/TD3, the update is dones as the same time as the gradient update
+            # see https://github.com/hill-a/stable-baselines/issues/900
+            self._on_step()
+
+            for idx, done in enumerate(dones):
+                if done:
+                    # Update stats
+                    num_collected_episodes += 1
+                    self._episode_num += 1
+
+                    if action_noise is not None:
+                        kwargs = dict(indices=[idx]) if env.num_envs > 1 else {}
+                        action_noise.reset(**kwargs)
+
+                    # Log training infos
+                    if log_interval is not None and self._episode_num % log_interval == 0:
+                        self._dump_logs()
+        callback.on_rollout_end()
+
+        return RolloutReturn(num_collected_steps * env.num_envs, num_collected_episodes, continue_training)

sbBrix/common/on_policy_algorithm.py

@@ -0,0 +1,265 @@
+from stable_baselines3.common.on_policy_algorithm import *
+
+
+class BetterOnPolicyAlgorithm(OnPolicyAlgorithm):
+    """
+    The base for On-Policy algorithms (ex: A2C/PPO).
+
+    :param policy: The policy model to use (MlpPolicy, CnnPolicy, ...)
+    :param env: The environment to learn from (if registered in Gym, can be str)
+    :param learning_rate: The learning rate, it can be a function
+        of the current progress remaining (from 1 to 0)
+    :param n_steps: The number of steps to run for each environment per update
+        (i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)
+    :param gamma: Discount factor
+    :param gae_lambda: Factor for trade-off of bias vs variance for Generalized Advantage Estimator.
+        Equivalent to classic advantage when set to 1.
+    :param ent_coef: Entropy coefficient for the loss calculation
+    :param vf_coef: Value function coefficient for the loss calculation
+    :param max_grad_norm: The maximum value for the gradient clipping
+    :param use_sde: Whether to use generalized State Dependent Exploration (gSDE)
+        instead of action noise exploration (default: False)
+    :param sde_sample_freq: Sample a new noise matrix every n steps when using gSDE
+        Default: -1 (only sample at the beginning of the rollout)
+    :param stats_window_size: Window size for the rollout logging, specifying the number of episodes to average
+        the reported success rate, mean episode length, and mean reward over
+    :param tensorboard_log: the log location for tensorboard (if None, no logging)
+    :param monitor_wrapper: When creating an environment, whether to wrap it
+        or not in a Monitor wrapper.
+    :param policy_kwargs: additional arguments to be passed to the policy on creation
+    :param verbose: Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for
+        debug messages
+    :param seed: Seed for the pseudo random generators
+    :param device: Device (cpu, cuda, ...) on which the code should be run.
+        Setting it to auto, the code will be run on the GPU if possible.
+    :param _init_setup_model: Whether or not to build the network at the creation of the instance
+    :param supported_action_spaces: The action spaces supported by the algorithm.
+    """
+
+    def __init__(
+        self,
+        policy: Union[str, Type[ActorCriticPolicy]],
+        env: Union[GymEnv, str],
+        learning_rate: Union[float, Schedule],
+        n_steps: int,
+        gamma: float,
+        gae_lambda: float,
+        ent_coef: float,
+        vf_coef: float,
+        max_grad_norm: float,
+        use_sde: bool,
+        sde_sample_freq: int,
+        use_pca: bool,
+        stats_window_size: int = 100,
+        tensorboard_log: Optional[str] = None,
+        monitor_wrapper: bool = True,
+        policy_kwargs: Optional[Dict[str, Any]] = None,
+        verbose: int = 0,
+        seed: Optional[int] = None,
+        device: Union[th.device, str] = "auto",
+        _init_setup_model: bool = True,
+        supported_action_spaces: Optional[Tuple[spaces.Space, ...]] = None,
+    ):
+        assert not (use_sde and use_pca)
+        self.use_pca = use_pca
+        super().__init__(
+            policy=policy,
+            env=env,
+            learning_rate=learning_rate,
+            n_steps=n_steps,
+            gamma=gamma,
+            gae_lambda=gae_lambda,
+            ent_coef=ent_coef,
+            vf_coef=vf_coef,
+            max_grad_norm=max_grad_norm,
+            policy_kwargs=policy_kwargs,
+            verbose=verbose,
+            device=device,
+            use_sde=use_sde,
+            sde_sample_freq=sde_sample_freq,
+            # support_multi_env=True,
+            seed=seed,
+            stats_window_size=stats_window_size,
+            tensorboard_log=tensorboard_log,
+            supported_action_spaces=supported_action_spaces,
+            monitor_wrapper=monitor_wrapper,
+            _init_setup_model=_init_setup_model
+        )
+
+    def _setup_model(self) -> None:
+        self._setup_lr_schedule()
+        self.set_random_seed(self.seed)
+
+        buffer_cls = DictRolloutBuffer if isinstance(self.observation_space, spaces.Dict) else RolloutBuffer
+
+        self.rollout_buffer = buffer_cls(
+            self.n_steps,
+            self.observation_space,
+            self.action_space,
+            device=self.device,
+            gamma=self.gamma,
+            gae_lambda=self.gae_lambda,
+            n_envs=self.n_envs,
+        )
+        self.policy = self.policy_class(  # pytype:disable=not-instantiable
+            self.observation_space,
+            self.action_space,
+            self.lr_schedule,
+            use_sde=self.use_sde,
+            use_pca=self.use_pca,
+            **self.policy_kwargs  # pytype:disable=not-instantiable
+        )
+        self.policy = self.policy.to(self.device)
+
+    def collect_rollouts(
+        self,
+        env: VecEnv,
+        callback: BaseCallback,
+        rollout_buffer: RolloutBuffer,
+        n_rollout_steps: int,
+    ) -> bool:
+        """
+        Collect experiences using the current policy and fill a ``RolloutBuffer``.
+        The term rollout here refers to the model-free notion and should not
+        be used with the concept of rollout used in model-based RL or planning.
+
+        :param env: The training environment
+        :param callback: Callback that will be called at each step
+            (and at the beginning and end of the rollout)
+        :param rollout_buffer: Buffer to fill with rollouts
+        :param n_rollout_steps: Number of experiences to collect per environment
+        :return: True if function returned with at least `n_rollout_steps`
+            collected, False if callback terminated rollout prematurely.
+        """
+        assert self._last_obs is not None, "No previous observation was provided"
+        # Switch to eval mode (this affects batch norm / dropout)
+        self.policy.set_training_mode(False)
+
+        n_steps = 0
+        rollout_buffer.reset()
+        # Sample new weights for the state dependent exploration
+        if self.use_sde:
+            self.policy.reset_noise(env.num_envs)
+
+        callback.on_rollout_start()
+
+        while n_steps < n_rollout_steps:
+            if self.use_sde and self.sde_sample_freq > 0 and n_steps % self.sde_sample_freq == 0:
+                # Sample a new noise matrix
+                self.policy.reset_noise(env.num_envs)
+
+            with th.no_grad():
+                # Convert to pytorch tensor or to TensorDict
+                obs_tensor = obs_as_tensor(self._last_obs, self.device)
+                actions, values, log_probs = self.policy(obs_tensor)
+            actions = actions.cpu().numpy()
+
+            # Rescale and perform action
+            clipped_actions = actions
+            # Clip the actions to avoid out of bound error
+            if isinstance(self.action_space, spaces.Box):
+                clipped_actions = np.clip(actions, self.action_space.low, self.action_space.high)
+
+            new_obs, rewards, dones, infos = env.step(clipped_actions)
+
+            self.num_timesteps += env.num_envs
+
+            # Give access to local variables
+            callback.update_locals(locals())
+            if callback.on_step() is False:
+                return False
+
+            self._update_info_buffer(infos)
+            n_steps += 1
+
+            if isinstance(self.action_space, spaces.Discrete):
+                # Reshape in case of discrete action
+                actions = actions.reshape(-1, 1)
+
+            # Handle timeout by bootstraping with value function
+            # see GitHub issue #633
+            for idx, done in enumerate(dones):
+                if (
+                    done
+                    and infos[idx].get("terminal_observation") is not None
+                    and infos[idx].get("TimeLimit.truncated", False)
+                ):
+                    terminal_obs = self.policy.obs_to_tensor(infos[idx]["terminal_observation"])[0]
+                    with th.no_grad():
+                        terminal_value = self.policy.predict_values(terminal_obs)[0]
+                    rewards[idx] += self.gamma * terminal_value
+
+            rollout_buffer.add(self._last_obs, actions, rewards, self._last_episode_starts, values, log_probs)
+            self._last_obs = new_obs
+            self._last_episode_starts = dones
+
+        with th.no_grad():
+            # Compute value for the last timestep
+            values = self.policy.predict_values(obs_as_tensor(new_obs, self.device))
+
+        rollout_buffer.compute_returns_and_advantage(last_values=values, dones=dones)
+
+        callback.on_rollout_end()
+
+        return True
+
+    def train(self) -> None:
+        """
+        Consume current rollout data and update policy parameters.
+        Implemented by individual algorithms.
+        """
+        raise NotImplementedError
+
+    def learn(
+        self: SelfOnPolicyAlgorithm,
+        total_timesteps: int,
+        callback: MaybeCallback = None,
+        log_interval: int = 1,
+        tb_log_name: str = "OnPolicyAlgorithm",
+        reset_num_timesteps: bool = True,
+        progress_bar: bool = False,
+    ) -> SelfOnPolicyAlgorithm:
+        iteration = 0
+
+        total_timesteps, callback = self._setup_learn(
+            total_timesteps,
+            callback,
+            reset_num_timesteps,
+            tb_log_name,
+            progress_bar,
+        )
+
+        callback.on_training_start(locals(), globals())
+
+        while self.num_timesteps < total_timesteps:
+            continue_training = self.collect_rollouts(self.env, callback, self.rollout_buffer, n_rollout_steps=self.n_steps)
+
+            if continue_training is False:
+                break
+
+            iteration += 1
+            self._update_current_progress_remaining(self.num_timesteps, total_timesteps)
+
+            # Display training infos
+            if log_interval is not None and iteration % log_interval == 0:
+                time_elapsed = max((time.time_ns() - self.start_time) / 1e9, sys.float_info.epsilon)
+                fps = int((self.num_timesteps - self._num_timesteps_at_start) / time_elapsed)
+                self.logger.record("time/iterations", iteration, exclude="tensorboard")
+                if len(self.ep_info_buffer) > 0 and len(self.ep_info_buffer[0]) > 0:
+                    self.logger.record("rollout/ep_rew_mean", safe_mean([ep_info["r"] for ep_info in self.ep_info_buffer]))
+                    self.logger.record("rollout/ep_len_mean", safe_mean([ep_info["l"] for ep_info in self.ep_info_buffer]))
+                self.logger.record("time/fps", fps)
+                self.logger.record("time/time_elapsed", int(time_elapsed), exclude="tensorboard")
+                self.logger.record("time/total_timesteps", self.num_timesteps, exclude="tensorboard")
+                self.logger.dump(step=self.num_timesteps)
+
+            self.train()
+
+        callback.on_training_end()
+
+        return self
+
+    def _get_torch_save_params(self) -> Tuple[List[str], List[str]]:
+        state_dicts = ["policy", "policy.optimizer"]
+
+        return state_dicts, []

sbBrix/ppo/ppo.py

@@ -6,15 +6,17 @@ import torch as th
 from gym import spaces
 from torch.nn import functional as F
 
-from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
+# from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
-from stable_baselines3.common.policies import ActorCriticCnnPolicy, ActorCriticPolicy, BasePolicy, MultiInputActorCriticPolicy
+from ..common.on_policy_algorithm import BetterOnPolicyAlgorithm
+# from stable_baselines3.common.policies import ActorCriticCnnPolicy, ActorCriticPolicy, BasePolicy, MultiInputActorCriticPolicy
+from ..common.policies import ActorCriticPolicy, BasePolicy
 from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
 from stable_baselines3.common.utils import explained_variance, get_schedule_fn
 
 SelfPPO = TypeVar("SelfPPO", bound="PPO")
 
 
-class PPO(OnPolicyAlgorithm):
+class PPO(BetterOnPolicyAlgorithm):
     """
     Proximal Policy Optimization algorithm (PPO) (clip version)
 
@@ -69,9 +71,7 @@ class PPO(OnPolicyAlgorithm):
     """
 
     policy_aliases: Dict[str, Type[BasePolicy]] = {
-        "MlpPolicy": ActorCriticPolicy,
+        "MlpPolicy": ActorCriticPolicy
-        "CnnPolicy": ActorCriticCnnPolicy,
-        "MultiInputPolicy": MultiInputActorCriticPolicy,
     }
 
     def __init__(
@@ -92,6 +92,7 @@ class PPO(OnPolicyAlgorithm):
         max_grad_norm: float = 0.5,
         use_sde: bool = False,
         sde_sample_freq: int = -1,
+        use_pca: bool = False,
         target_kl: Optional[float] = None,
         stats_window_size: int = 100,
         tensorboard_log: Optional[str] = None,
@@ -113,6 +114,7 @@ class PPO(OnPolicyAlgorithm):
             max_grad_norm=max_grad_norm,
             use_sde=use_sde,
             sde_sample_freq=sde_sample_freq,
+            use_pca=use_pca,
             stats_window_size=stats_window_size,
             tensorboard_log=tensorboard_log,
             policy_kwargs=policy_kwargs,
@@ -315,4 +317,3 @@ class PPO(OnPolicyAlgorithm):
             reset_num_timesteps=reset_num_timesteps,
             progress_bar=progress_bar,
         )
-

sbBrix/sac/sac.py

@@ -7,7 +7,8 @@ from torch.nn import functional as F
 
 from stable_baselines3.common.buffers import ReplayBuffer
 from stable_baselines3.common.noise import ActionNoise
-from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
+# from stable_baselines3.common.off_policy_algorithm import OffPolicyAlgorithm
+from common.off_policy_algorithm import BetterOffPolicyAlgorithm
 from stable_baselines3.common.policies import BasePolicy
 from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
 from stable_baselines3.common.utils import get_parameters_by_name, polyak_update
@@ -16,7 +17,7 @@ from stable_baselines3.sac.policies import CnnPolicy, MlpPolicy, MultiInputPolic
 SelfSAC = TypeVar("SelfSAC", bound="SAC")
 
 
-class SAC(OffPolicyAlgorithm):
+class SAC(BetterOffPolicyAlgorithm):
     """
     Soft Actor-Critic (SAC)
     Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor,
@@ -321,4 +322,3 @@ class SAC(OffPolicyAlgorithm):
         else:
             saved_pytorch_variables = ["ent_coef_tensor"]
         return state_dicts, saved_pytorch_variables
-