[RLlib] Fix Atari learning test regressions (2 bugs) and 1 minor attention net bug.

release/rllib_tests/learning_tests/hard_learning_tests.yaml

@@ -52,6 +52,33 @@ apex-breakoutnoframeskip-v4:
         target_network_update_freq: 50000
         timesteps_per_iteration: 25000
 
+appo-pong-no-frameskip-v4:
+    env: PongNoFrameskip-v4
+    run: APPO
+    # Minimum reward and total ts (in given time_total_s) to pass this test.
+    pass_criteria:
+        episode_reward_mean: 18.0
+        timesteps_total: 5000000
+    stop:
+        time_total_s: 2000
+    config:
+        vtrace: True
+        use_kl_loss: False
+        rollout_fragment_length: 50
+        train_batch_size: 750
+        num_workers: 31
+        broadcast_interval: 1
+        max_sample_requests_in_flight_per_worker: 1
+        num_multi_gpu_tower_stacks: 1
+        num_envs_per_worker: 8
+        num_sgd_iter: 2
+        vf_loss_coeff: 1.0
+        clip_param: 0.3
+        num_gpus: 1
+        grad_clip: 10
+        model:
+          dim: 42
+
 ddpg-hopperbulletenv-v0:
     env: HopperBulletEnv-v0
     run: DDPG

rllib/BUILD

@@ -1845,7 +1845,7 @@ py_test(
     tags = ["team:ml", "examples", "examples_A"],
     size = "medium",
     srcs = ["examples/attention_net.py"],
-    args = ["--as-test", "--stop-reward=20"]
+    args = ["--as-test", "--stop-reward=60"]
 )
 
 py_test(
@@ -1854,7 +1854,7 @@ py_test(
     tags = ["team:ml", "examples", "examples_A"],
     size = "medium",
     srcs = ["examples/attention_net.py"],
-    args = ["--as-test", "--stop-reward=20", "--framework torch"]
+    args = ["--as-test", "--stop-reward=60", "--framework torch"]
 )
 
 py_test(

rllib/agents/pg/tests/test_pg.py

@@ -12,10 +12,12 @@
 
 
 class TestPG(unittest.TestCase):
-    def setUp(self):
+    @classmethod
+    def setUpClass(cls) -> None:
         ray.init()
 
-    def tearDown(self):
+    @classmethod
+    def tearDownClass(cls) -> None:
         ray.shutdown()
 
     def test_pg_compilation(self):

rllib/env/utils.py

@@ -61,7 +61,7 @@ def gym_env_creator(env_context: EnvContext, env_descriptor: str):
    For VizDoom support: Install VizDoom
    (https://github.com/mwydmuch/ViZDoom/blob/master/doc/Building.md) and
    `pip install vizdoomgym`.
-   For PyBullet support: `pip install pybullet pybullet_envs`.
+   For PyBullet support: `pip install pybullet`.
 b) To register your custom env, do `from ray import tune;
    tune.register('[name]', lambda cfg: [return env obj from here using cfg])`.
    Then in your config, do `config['env'] = [name]`.

rllib/env/wrappers/atari_wrappers.py

@@ -283,17 +283,13 @@ def observation(self, observation):
         return np.array(observation).astype(np.float32) / 255.0
 
 
-def wrap_deepmind(
-        env,
-        dim=84,
-        # TODO: (sven) Remove once traj. view is norm.
-        framestack=True,
-        framestack_via_traj_view_api=False):
+def wrap_deepmind(env, dim=84, framestack=True):
     """Configure environment for DeepMind-style Atari.
 
     Note that we assume reward clipping is done outside the wrapper.
 
     Args:
+        env (EnvType): The env object to wrap.
         dim (int): Dimension to resize observations to (dim x dim).
         framestack (bool): Whether to framestack observations.
     """
@@ -307,12 +303,7 @@ def wrap_deepmind(
     env = WarpFrame(env, dim)
     # env = ScaledFloatFrame(env)  # TODO: use for dqn?
     # env = ClipRewardEnv(env)  # reward clipping is handled by policy eval
-    # New way of frame stacking via the trajectory view API (model config key:
-    # `num_framestacks=[int]`.
-    if framestack_via_traj_view_api:
-        env = FrameStackTrajectoryView(env)
-    # Old way (w/o traj. view API) via model config key: `framestack=True`.
-    # TODO: (sven) Remove once traj. view is norm.
-    elif framestack is True:
+    # 4x image framestacking.
+    if framestack is True:
         env = FrameStack(env, 4)
     return env

rllib/evaluation/collectors/simple_list_collector.py

@@ -192,8 +192,10 @@ def build(self, view_requirements: ViewRequirementsDict) -> SampleBatch:
                         d.itemsize * int(np.product(d.shape[i + 1:]))
                         for i in range(1, len(d.shape))
                     ]
+                    start = self.shift_before - shift_win + 1 + obs_shift + \
+                        view_req.shift_to
                     data = np.lib.stride_tricks.as_strided(
-                        d[self.shift_before - shift_win:],
+                        d[start:start + self.agent_steps],
                         [self.agent_steps, shift_win
                          ] + [d.shape[i] for i in range(1, len(d.shape))],
                         [data_size, data_size] + strides)

rllib/evaluation/rollout_worker.py

@@ -31,7 +31,7 @@
 from ray.rllib.utils import force_list, merge_dicts
 from ray.rllib.utils.annotations import DeveloperAPI
 from ray.rllib.utils.debug import summarize, update_global_seed_if_necessary
-from ray.rllib.utils.deprecation import DEPRECATED_VALUE, deprecation_warning
+from ray.rllib.utils.deprecation import deprecation_warning
 from ray.rllib.utils.error import EnvError
 from ray.rllib.utils.filter import get_filter, Filter
 from ray.rllib.utils.framework import try_import_tf, try_import_torch
@@ -400,7 +400,8 @@ def gen_rollouts():
             self.callbacks: DefaultCallbacks = DefaultCallbacks()
         self.worker_index: int = worker_index
         self.num_workers: int = num_workers
-        model_config: ModelConfigDict = model_config or {}
+        model_config: ModelConfigDict = \
+            model_config or self.policy_config.get("model") or {}
 
         # Default policy mapping fn is to always return DEFAULT_POLICY_ID,
         # independent on the agent ID and the episode passed in.
@@ -464,27 +465,14 @@ def wrap(env):
                 if clip_rewards is None:
                     clip_rewards = True
 
-                # Deprecated way of framestacking is used.
-                framestack = model_config.get("framestack") is True
-                # framestacking via trajectory view API is enabled.
-                num_framestacks = model_config.get("num_framestacks", 0)
-
-                # Trajectory view API is on and num_framestacks=auto:
-                # Only stack traj. view based if old
-                # `framestack=[invalid value]`.
-                if num_framestacks == "auto":
-                    if framestack == DEPRECATED_VALUE:
-                        model_config["num_framestacks"] = num_framestacks = 4
-                    else:
-                        model_config["num_framestacks"] = num_framestacks = 0
-                framestack_traj_view = num_framestacks > 1
+                # Framestacking is used.
+                use_framestack = model_config.get("framestack") is True
 
                 def wrap(env):
                     env = wrap_deepmind(
                         env,
                         dim=model_config.get("dim"),
-                        framestack=framestack,
-                        framestack_via_traj_view_api=framestack_traj_view)
+                        framestack=use_framestack)
                     env = record_env_wrapper(env, record_env, log_dir,
                                              policy_config)
                     return env
@@ -740,7 +728,8 @@ def sample(self) -> SampleBatchType:
             return self.last_batch
         elif self.input_reader is None:
             raise ValueError("RolloutWorker has no `input_reader` object! "
-                             "Cannot call `sample()`.")
+                             "Cannot call `sample()`. You can try setting "
+                             "`create_env_on_driver` to True.")
 
         if log_once("sample_start"):
             logger.info("Generating sample batch of size {}".format(
@@ -1423,6 +1412,8 @@ def _determine_spaces_for_multi_agent_dict(
         policy_config: Optional[PartialTrainerConfigDict] = None,
 ) -> MultiAgentPolicyConfigDict:
 
+    policy_config = policy_config or {}
+
     # Try extracting spaces from env or from given spaces dict.
     env_obs_space = None
     env_act_space = None
@@ -1455,14 +1446,15 @@ def _determine_spaces_for_multi_agent_dict(
                 obs_space = spaces[pid][0]
             elif env_obs_space is not None:
                 obs_space = env_obs_space
-            elif policy_config and policy_config.get("observation_space"):
+            elif policy_config.get("observation_space"):
                 obs_space = policy_config["observation_space"]
             else:
                 raise ValueError(
                     "`observation_space` not provided in PolicySpec for "
                     f"{pid} and env does not have an observation space OR "
                     "no spaces received from other workers' env(s) OR no "
                     "`observation_space` specified in config!")
+
             multi_agent_dict[pid] = multi_agent_dict[pid]._replace(
                 observation_space=obs_space)
 
@@ -1471,7 +1463,7 @@ def _determine_spaces_for_multi_agent_dict(
                 act_space = spaces[pid][1]
             elif env_act_space is not None:
                 act_space = env_act_space
-            elif policy_config and policy_config.get("action_space"):
+            elif policy_config.get("action_space"):
                 act_space = policy_config["action_space"]
             else:
                 raise ValueError(
@@ -1489,8 +1481,7 @@ def _validate_env(env: EnvType, env_context: EnvContext = None):
     msg = f"Validating sub-env at vector index={env_context.vector_index} ..."
 
     allowed_types = [
-        gym.Env, MultiAgentEnv, ExternalEnv, VectorEnv, BaseEnv,
-        ray.actor.ActorHandle
+        gym.Env, ExternalEnv, VectorEnv, BaseEnv, ray.actor.ActorHandle
     ]
     if not any(isinstance(env, tpe) for tpe in allowed_types):
         # Allow this as a special case (assumed gym.Env).
@@ -1508,7 +1499,7 @@ def _validate_env(env: EnvType, env_context: EnvContext = None):
                 f"(type={type(env)}).")
 
     # Do some test runs with the provided env.
-    if isinstance(env, gym.Env):
+    if isinstance(env, gym.Env) and not isinstance(env, MultiAgentEnv):
         # Make sure the gym.Env has the two space attributes properly set.
         assert hasattr(env, "observation_space") and hasattr(
             env, "action_space")

rllib/evaluation/worker_set.py

@@ -418,7 +418,6 @@ def valid_module(class_path):
             normalize_actions=config["normalize_actions"],
             clip_actions=config["clip_actions"],
             env_config=config["env_config"],
-            model_config=config["model"],
             policy_config=config,
             worker_index=worker_index,
             num_workers=num_workers,

rllib/models/catalog.py

@@ -131,17 +131,8 @@
     "attention_use_n_prev_rewards": 0,
 
     # == Atari ==
-    # Which framestacking size to use for Atari envs.
-    # "auto": Use a value of 4, but only if the env is an Atari env.
-    # > 1: Use the trajectory view API in the default VisionNets to request the
-    #      last n observations (single, grayscaled 84x84 image frames) as
-    #      inputs. The time axis in the so provided observation tensors
-    #      will come right after the batch axis (channels first format),
-    #      e.g. BxTx84x84, where T=num_framestacks.
-    # 0 or 1: No framestacking used.
-    # Use the deprecated `framestack=True`, to disable the above behavor and to
-    # enable legacy stacking behavior (w/o trajectory view API) instead.
-    "num_framestacks": "auto",
+    # Set to True to enable 4x stacking behavior.
+    "framestack": True,
     # Final resized frame dimension
     "dim": 84,
     # (deprecated) Converts ATARI frame to 1 Channel Grayscale image
@@ -166,8 +157,6 @@
     # Deprecated keys:
     # Use `lstm_use_prev_action` or `lstm_use_prev_reward` instead.
     "lstm_use_prev_action_reward": DEPRECATED_VALUE,
-    # Use `num_framestacks` (int) instead.
-    "framestack": True,
 }
 # __sphinx_doc_end__
 # yapf: enable
@@ -807,10 +796,6 @@ def _get_v2_model_class(input_space: gym.Space,
                 "framework={} not supported in `ModelCatalog._get_v2_model_"
                 "class`!".format(framework))
 
-        # Discrete/1D obs-spaces or 2D obs space but traj. view framestacking
-        # disabled.
-        num_framestacks = model_config.get("num_framestacks", "auto")
-
         # Tuple space, where at least one sub-space is image.
         # -> Complex input model.
         space_to_check = input_space if not hasattr(
@@ -824,8 +809,7 @@ def _get_v2_model_class(input_space: gym.Space,
         # Single, flattenable/one-hot-able space -> Simple FCNet.
         if isinstance(input_space, (Discrete, MultiDiscrete)) or \
                 len(input_space.shape) == 1 or (
-                len(input_space.shape) == 2 and (
-                num_framestacks == "auto" or num_framestacks <= 1)):
+                len(input_space.shape) == 2):
             # Keras native requested AND no auto-rnn-wrapping.
             if model_config.get("_use_default_native_models") and Keras_FCNet:
                 return Keras_FCNet
@@ -886,10 +870,3 @@ def _validate_config(config: ModelConfigDict, framework: str) -> None:
             elif config.get("use_lstm"):
                 raise ValueError("`use_lstm` not available for "
                                  "framework=jax so far!")
-
-        if config.get("framestack") != DEPRECATED_VALUE:
-            # deprecation_warning(
-            #     old="framestack", new="num_framestacks (int)", error=False)
-            # If old behavior is desired, disable traj. view-style
-            # framestacking.
-            config["num_framestacks"] = 0