[RLlib] New ConnectorV2 API #02: SingleAgentEpisode enhancements.

rllib/BUILD

@@ -683,6 +683,16 @@ py_test(
     args = ["--dir=tuned_examples/ppo"]
 )
 
+py_test(
+    name = "test_memory_leak_ppo_new_stack",
+    tags = ["team:rllib", "memory_leak_tests"],
+    main = "utils/tests/run_memory_leak_tests.py",
+    size = "large",
+    srcs = ["utils/tests/run_memory_leak_tests.py"],
+    data = ["tuned_examples/ppo/memory-leak-test-ppo-new-stack.py"],
+    args = ["--dir=tuned_examples/ppo", "--to-check=rollout_worker"]
+)
+
 py_test(
     name = "test_memory_leak_sac",
     tags = ["team:rllib", "memory_leak_tests"],
@@ -818,19 +828,26 @@ sh_test(
 # )
 
 py_test(
-    name = "env/tests/test_single_agent_gym_env_runner",
+    name = "env/tests/test_single_agent_env_runner",
     tags = ["team:rllib", "env"],
     size = "medium",
-    srcs = ["env/tests/test_single_agent_gym_env_runner.py"]
+    srcs = ["env/tests/test_single_agent_env_runner.py"]
 )
 
 py_test(
     name = "env/tests/test_single_agent_episode",
     tags = ["team:rllib", "env"],
-    size = "medium",
+    size = "small",
     srcs = ["env/tests/test_single_agent_episode.py"]
 )
 
+py_test(
+    name = "env/tests/test_lookback_buffer",
+    tags = ["team:rllib", "env"],
+    size = "small",
+    srcs = ["env/tests/test_lookback_buffer.py"]
+)
+
 py_test(
     name = "env/wrappers/tests/test_exception_wrapper",
     tags = ["team:rllib", "env"],
@@ -1332,7 +1349,6 @@ py_test(
 # Tag: utils
 # --------------------------------------------------------------------
 
-# Checkpoint Utils
 py_test(
     name = "test_checkpoint_utils",
     tags = ["team:rllib", "utils"],

rllib/algorithms/dreamerv3/utils/env_runner.py

@@ -32,6 +32,9 @@
 _, tf, _ = try_import_tf()
 
 
+# TODO (sven): Use SingleAgentEnvRunner instead of this as soon as we have the new
+#  ConnectorV2 example classes to make Atari work properly with these (w/o requiring the
+#  classes at the bottom of this file here, e.g. `ActionClip`).
 class DreamerV3EnvRunner(EnvRunner):
     """An environment runner to collect data from vectorized gymnasium environments."""
 
@@ -144,6 +147,7 @@ def __init__(
 
         self._needs_initial_reset = True
         self._episodes = [None for _ in range(self.num_envs)]
+        self._states = [None for _ in range(self.num_envs)]
 
         # TODO (sven): Move metrics temp storage and collection out of EnvRunner
         #  and RolloutWorkers. These classes should not continue tracking some data
@@ -254,10 +258,8 @@ def _sample_timesteps(
 
             # Set initial obs and states in the episodes.
             for i in range(self.num_envs):
-                self._episodes[i].add_initial_observation(
-                    initial_observation=obs[i],
-                    initial_state={k: s[i] for k, s in states.items()},
-                )
+                self._episodes[i].add_env_reset(observation=obs[i])
+                self._states[i] = {k: s[i] for k, s in states.items()}
         # Don't reset existing envs; continue in already started episodes.
         else:
             # Pick up stored observations and states from previous timesteps.
@@ -268,7 +270,9 @@ def _sample_timesteps(
             states = {
                 k: np.stack(
                     [
-                        initial_states[k][i] if eps.states is None else eps.states[k]
+                        initial_states[k][i]
+                        if self._states[i] is None
+                        else self._states[i][k]
                         for i, eps in enumerate(self._episodes)
                     ]
                 )
@@ -278,7 +282,7 @@ def _sample_timesteps(
             # to 1.0, otherwise 0.0.
             is_first = np.zeros((self.num_envs,))
             for i, eps in enumerate(self._episodes):
-                if eps.states is None:
+                if len(eps) == 0:
                     is_first[i] = 1.0
 
         # Loop through env for n timesteps.
@@ -319,37 +323,39 @@ def _sample_timesteps(
                 if terminateds[i] or truncateds[i]:
                     # Finish the episode with the actual terminal observation stored in
                     # the info dict.
-                    self._episodes[i].add_timestep(
-                        infos["final_observation"][i],
-                        actions[i],
-                        rewards[i],
-                        state=s,
+                    self._episodes[i].add_env_step(
+                        observation=infos["final_observation"][i],
+                        action=actions[i],
+                        reward=rewards[i],
                         is_terminated=terminateds[i],
                         is_truncated=truncateds[i],
                     )
+                    self._states[i] = s
                     # Reset h-states to the model's initial ones b/c we are starting a
                     # new episode.
                     for k, v in self.module.get_initial_state().items():
                         states[k][i] = v.numpy()
                     is_first[i] = True
                     done_episodes_to_return.append(self._episodes[i])
                     # Create a new episode object.
-                    self._episodes[i] = SingleAgentEpisode(
-                        observations=[obs[i]], states=s
-                    )
+                    self._episodes[i] = SingleAgentEpisode(observations=[obs[i]])
                 else:
-                    self._episodes[i].add_timestep(
-                        obs[i], actions[i], rewards[i], state=s
+                    self._episodes[i].add_env_step(
+                        observation=obs[i],
+                        action=actions[i],
+                        reward=rewards[i],
                     )
                     is_first[i] = False
 
+                self._states[i] = s
+
         # Return done episodes ...
         self._done_episodes_for_metrics.extend(done_episodes_to_return)
         # ... and all ongoing episode chunks. Also, make sure, we return
         # a copy and start new chunks so that callers of this function
         # don't alter our ongoing and returned Episode objects.
         ongoing_episodes = self._episodes
-        self._episodes = [eps.create_successor() for eps in self._episodes]
+        self._episodes = [eps.cut() for eps in self._episodes]
         for eps in ongoing_episodes:
             self._ongoing_episodes_for_metrics[eps.id_].append(eps)
 
@@ -385,11 +391,11 @@ def _sample_episodes(
             render_images = [e.render() for e in self.env.envs]
 
         for i in range(self.num_envs):
-            episodes[i].add_initial_observation(
-                initial_observation=obs[i],
-                initial_state={k: s[i] for k, s in states.items()},
-                initial_render_image=render_images[i],
+            episodes[i].add_env_reset(
+                observation=obs[i],
+                render_image=render_images[i],
             )
+            self._states[i] = {k: s[i] for k, s in states.items()}
 
         eps = 0
         while eps < num_episodes:
@@ -425,14 +431,14 @@ def _sample_episodes(
                 if terminateds[i] or truncateds[i]:
                     eps += 1
 
-                    episodes[i].add_timestep(
-                        infos["final_observation"][i],
-                        actions[i],
-                        rewards[i],
-                        state=s,
+                    episodes[i].add_env_step(
+                        observation=infos["final_observation"][i],
+                        action=actions[i],
+                        reward=rewards[i],
                         is_terminated=terminateds[i],
                         is_truncated=truncateds[i],
                     )
+                    self._states[i] = s
                     done_episodes_to_return.append(episodes[i])
 
                     # Also early-out if we reach the number of episodes within this
@@ -452,13 +458,13 @@ def _sample_episodes(
                         render_images=[render_images[i]],
                     )
                 else:
-                    episodes[i].add_timestep(
-                        obs[i],
-                        actions[i],
-                        rewards[i],
-                        state=s,
+                    episodes[i].add_env_step(
+                        observation=obs[i],
+                        action=actions[i],
+                        reward=rewards[i],
                         render_image=render_images[i],
                     )
+                    self._states[i] = s
                     is_first[i] = False
 
         self._done_episodes_for_metrics.extend(done_episodes_to_return)