overriding collect_rollouts

sb3_trl/trl_pg/trl_pg.py

@@ -10,6 +10,10 @@ from stable_baselines3.common.on_policy_algorithm import OnPolicyAlgorithm
 from stable_baselines3.common.policies import ActorCriticCnnPolicy, ActorCriticPolicy, BasePolicy, MultiInputActorCriticPolicy
 from stable_baselines3.common.type_aliases import GymEnv, MaybeCallback, Schedule
 from stable_baselines3.common.utils import explained_variance, get_schedule_fn
+from stable_baselines3.common.vec_env import VecEnv
+from stable_baselines3.common.buffers import RolloutBuffer
+from stable_baselines3.common.callbacks import BaseCallback
+from stable_baselines3.common.utils import obs_as_tensor
 
 
 class TRL_PG(OnPolicyAlgorithm):
@@ -379,3 +383,102 @@ class TRL_PG(OnPolicyAlgorithm):
             eval_log_path=eval_log_path,
             reset_num_timesteps=reset_num_timesteps,
         )
+
+    # This is new compared to PPO.
+    # TRL requires us to also save the original mean and std in our rollouts
+    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_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, gym.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, gym.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