[RLlib] Fix issues with action masking examples

rllib/BUILD

@@ -2977,12 +2977,12 @@ py_test(
 # --------------------------------------------------------------------
 
 py_test(
-    name = "examples/action_masking_tf",
+    name = "examples/action_masking_tf2",
     main = "examples/action_masking.py",
     tags = ["team:rllib", "exclusive", "examples"],
     size = "small",
     srcs = ["examples/action_masking.py"],
-    args = ["--stop-iter=2", "--framework=tf"]
+    args = ["--stop-iter=2", "--framework=tf2"]
 )
 
 py_test(

rllib/examples/action_masking.py

@@ -41,31 +41,21 @@
 
 from gymnasium.spaces import Box, Discrete
 import ray
-from ray import air, tune
 from ray.rllib.algorithms import ppo
 from ray.rllib.examples.env.action_mask_env import ActionMaskEnv
-from ray.rllib.examples.models.action_mask_model import (
-    ActionMaskModel,
-    TorchActionMaskModel,
+from ray.rllib.examples.rl_module.action_masking_rlm import (
+    TorchActionMaskRLM,
+    TFActionMaskRLM,
 )
+from ray.rllib.core.rl_module.rl_module import SingleAgentRLModuleSpec
+
 from ray.tune.logger import pretty_print
 
 
 def get_cli_args():
     """Create CLI parser and return parsed arguments"""
     parser = argparse.ArgumentParser()
 
-    # example-specific args
-    parser.add_argument(
-        "--no-masking",
-        action="store_true",
-        help="Do NOT mask invalid actions. This will likely lead to errors.",
-    )
-
-    # general args
-    parser.add_argument(
-        "--run", type=str, default="APPO", help="The RLlib-registered algorithm to use."
-    )
     parser.add_argument("--num-cpus", type=int, default=0)
     parser.add_argument(
         "--framework",
@@ -76,24 +66,6 @@ def get_cli_args():
     parser.add_argument(
         "--stop-iters", type=int, default=10, help="Number of iterations to train."
     )
-    parser.add_argument(
-        "--stop-timesteps",
-        type=int,
-        default=10000,
-        help="Number of timesteps to train.",
-    )
-    parser.add_argument(
-        "--stop-reward",
-        type=float,
-        default=80.0,
-        help="Reward at which we stop training.",
-    )
-    parser.add_argument(
-        "--no-tune",
-        action="store_true",
-        help="Run without Tune using a manual train loop instead. Here,"
-        "there is no TensorBoard support.",
-    )
     parser.add_argument(
         "--local-mode",
         action="store_true",
@@ -110,6 +82,15 @@ def get_cli_args():
 
     ray.init(num_cpus=args.num_cpus or None, local_mode=args.local_mode)
 
+    if args.framework == "torch":
+        rlm_class = TorchActionMaskRLM
+    elif args.framework == "tf2":
+        rlm_class = TFActionMaskRLM
+    else:
+        raise ValueError(f"Unsupported framework: {args.framework}")
+
+    rlm_spec = SingleAgentRLModuleSpec(module_class=rlm_class)
+
     # main part: configure the ActionMaskEnv and ActionMaskModel
     config = (
         ppo.PPOConfig()
@@ -119,75 +100,47 @@ def get_cli_args():
             ActionMaskEnv,
             env_config={
                 "action_space": Discrete(100),
+                # This is not going to be the observation space that our RLModule sees.
+                # It's only the configuration provided to the environment.
+                # The environment will instead create Dict observations with
+                # the keys "observations" and "action_mask".
                 "observation_space": Box(-1.0, 1.0, (5,)),
             },
         )
-        .training(
-            # the ActionMaskModel retrieves the invalid actions and avoids them
-            model={
-                "custom_model": ActionMaskModel
-                if args.framework != "torch"
-                else TorchActionMaskModel,
-                # disable action masking according to CLI
-                "custom_model_config": {"no_masking": args.no_masking},
-            },
-        )
+        # We need to disable preprocessing of observations, because preprocessing
+        # would flatten the observation dict of the environment.
+        .experimental(_disable_preprocessor_api=True)
         .framework(args.framework)
         .resources(
             # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
             num_gpus=int(os.environ.get("RLLIB_NUM_GPUS", "0"))
         )
+        .rl_module(rl_module_spec=rlm_spec)
     )
 
-    stop = {
-        "training_iteration": args.stop_iters,
-        "timesteps_total": args.stop_timesteps,
-        "episode_reward_mean": args.stop_reward,
-    }
-
-    # manual training loop (no Ray tune)
-    if args.no_tune:
-        if args.run not in {"APPO", "PPO"}:
-            raise ValueError("This example only supports APPO and PPO.")
-
-        algo = config.build()
-
-        # run manual training loop and print results after each iteration
-        for _ in range(args.stop_iters):
-            result = algo.train()
-            print(pretty_print(result))
-            # stop training if the target train steps or reward are reached
-            if (
-                result["timesteps_total"] >= args.stop_timesteps
-                or result["episode_reward_mean"] >= args.stop_reward
-            ):
-                break
-
-        # manual test loop
-        print("Finished training. Running manual test/inference loop.")
-        # prepare environment with max 10 steps
-        config["env_config"]["max_episode_len"] = 10
-        env = ActionMaskEnv(config["env_config"])
-        obs, info = env.reset()
-        done = False
-        # run one iteration until done
-        print(f"ActionMaskEnv with {config['env_config']}")
-        while not done:
-            action = algo.compute_single_action(obs)
-            next_obs, reward, done, truncated, _ = env.step(action)
-            # observations contain original observations and the action mask
-            # reward is random and irrelevant here and therefore not printed
-            print(f"Obs: {obs}, Action: {action}")
-            obs = next_obs
-
-    # Run with tune for auto Algorithm creation, stopping, TensorBoard, etc.
-    else:
-        tuner = tune.Tuner(
-            args.run,
-            param_space=config.to_dict(),
-            run_config=air.RunConfig(stop=stop, verbose=2),
-        )
-        tuner.fit()
+    algo = config.build()
+
+    # run manual training loop and print results after each iteration
+    for _ in range(args.stop_iters):
+        result = algo.train()
+        print(pretty_print(result))
+
+    # manual test loop
+    print("Finished training. Running manual test/inference loop.")
+    # prepare environment with max 10 steps
+    config["env_config"]["max_episode_len"] = 10
+    env = ActionMaskEnv(config["env_config"])
+    obs, info = env.reset()
+    done = False
+    # run one iteration until done
+    print(f"ActionMaskEnv with {config['env_config']}")
+    while not done:
+        action = algo.compute_single_action(obs)
+        next_obs, reward, done, truncated, _ = env.step(action)
+        # observations contain original observations and the action mask
+        # reward is random and irrelevant here and therefore not printed
+        print(f"Obs: {obs}, Action: {action}")
+        obs = next_obs
 
     print("Finished successfully without selecting invalid actions.")
     ray.shutdown()

rllib/examples/rl_module/action_masking_rlm.py

@@ -0,0 +1,121 @@
+import gymnasium as gym
+
+from ray.rllib.algorithms.ppo.tf.ppo_tf_rl_module import PPOTfRLModule
+from ray.rllib.algorithms.ppo.torch.ppo_torch_rl_module import PPOTorchRLModule
+from ray.rllib.core.rl_module.rl_module import RLModule, RLModuleConfig
+from ray.rllib.policy.sample_batch import SampleBatch
+from ray.rllib.utils.framework import try_import_torch, try_import_tf
+from ray.rllib.utils.torch_utils import FLOAT_MIN
+
+torch, nn = try_import_torch()
+_, tf, _ = try_import_tf()
+
+
+class ActionMaskRLMBase(RLModule):
+    def __init__(self, config: RLModuleConfig):
+        if not isinstance(config.observation_space, gym.spaces.Dict):
+            raise ValueError(
+                "This model requires the environment to provide a "
+                "gym.spaces.Dict observation space."
+            )
+        # We need to adjust the observation space for this RL Module so that, when
+        # building the default models, the RLModule does not "see" the action mask but
+        # only the original observation space without the action mask. This tricks it
+        # into building models that are compatible with the original observation space.
+        config.observation_space = config.observation_space["observations"]
+
+        # The PPORLModule, in its constructor, will build models for the modified
+        # observation space.
+        super().__init__(config)
+
+
+class TorchActionMaskRLM(ActionMaskRLMBase, PPOTorchRLModule):
+    def _forward_inference(self, batch, **kwargs):
+        return mask_forward_fn_torch(super()._forward_inference, batch, **kwargs)
+
+    def _forward_train(self, batch, *args, **kwargs):
+        return mask_forward_fn_torch(super()._forward_train, batch, **kwargs)
+
+    def _forward_exploration(self, batch, *args, **kwargs):
+        return mask_forward_fn_torch(super()._forward_exploration, batch, **kwargs)
+
+
+class TFActionMaskRLM(ActionMaskRLMBase, PPOTfRLModule):
+    def _forward_inference(self, batch, **kwargs):
+        return mask_forward_fn_tf(super()._forward_inference, batch, **kwargs)
+
+    def _forward_train(self, batch, *args, **kwargs):
+        return mask_forward_fn_tf(super()._forward_train, batch, **kwargs)
+
+    def _forward_exploration(self, batch, *args, **kwargs):
+        return mask_forward_fn_tf(super()._forward_exploration, batch, **kwargs)
+
+
+def mask_forward_fn_torch(forward_fn, batch, **kwargs):
+    _check_batch(batch)
+
+    # Extract the available actions tensor from the observation.
+    action_mask = batch[SampleBatch.OBS]["action_mask"]
+
+    # Modify the incoming batch so that the default models can compute logits and
+    # values as usual.
+    batch[SampleBatch.OBS] = batch[SampleBatch.OBS]["observations"]
+
+    outputs = forward_fn(batch, **kwargs)
+
+    # Mask logits
+    logits = outputs[SampleBatch.ACTION_DIST_INPUTS]
+    # Convert action_mask into a [0.0 || -inf]-type mask.
+    inf_mask = torch.clamp(torch.log(action_mask), min=FLOAT_MIN)
+    masked_logits = logits + inf_mask
+
+    # Replace original values with masked values.
+    outputs[SampleBatch.ACTION_DIST_INPUTS] = masked_logits
+
+    return outputs
+
+
+def mask_forward_fn_tf(forward_fn, batch, **kwargs):
+    _check_batch(batch)
+
+    # Extract the available actions tensor from the observation.
+    action_mask = batch[SampleBatch.OBS]["action_mask"]
+
+    # Modify the incoming batch so that the default models can compute logits and
+    # values as usual.
+    batch[SampleBatch.OBS] = batch[SampleBatch.OBS]["observations"]
+
+    outputs = forward_fn(batch, **kwargs)
+
+    # Mask logits
+    logits = outputs[SampleBatch.ACTION_DIST_INPUTS]
+    # Convert action_mask into a [0.0 || -inf]-type mask.
+    inf_mask = tf.maximum(tf.math.log(action_mask), tf.float32.min)
+    masked_logits = logits + inf_mask
+
+    # Replace original values with masked values.
+    outputs[SampleBatch.ACTION_DIST_INPUTS] = masked_logits
+
+    return outputs
+
+
+def _check_batch(batch):
+    """Check whether the batch contains the required keys."""
+    if "action_mask" not in batch[SampleBatch.OBS]:
+        raise ValueError(
+            "Action mask not found in observation. This model requires "
+            "the environment to provide observations that include an "
+            "action mask (i.e. an observation space of the Dict space "
+            "type that looks as follows: \n"
+            "{'action_mask': Box(0.0, 1.0, shape=(self.action_space.n,)),"
+            "'observations': <observation_space>}"
+        )
+    if "observations" not in batch[SampleBatch.OBS]:
+        raise ValueError(
+            "Observations not found in observation.This model requires "
+            "the environment to provide observations that include a "
+            " (i.e. an observation space of the Dict space "
+            "type that looks as follows: \n"
+            "{'action_mask': Box(0.0, 1.0, shape=(self.action_space.n,)),"
+            "'observations': <observation_space>}"
+        )

rllib/models/tests/test_distributions.py

@@ -129,16 +129,6 @@ def test_categorical(self):
         expected = (probs * (probs / probs2).log()).sum(dim=-1)
         check(dist_with_probs.kl(dist2), expected)
 
-        # test temperature
-        dist_with_logits = TorchCategorical(logits=logits, temperature=1e-20)
-        samples = dist_with_logits.sample()
-        rsamples = dist_with_logits.rsample()
-        # expected is armax of logits
-        expected = logits.argmax(dim=-1)
-        check(samples, expected)
-        # rsample should be the same as sample, but one-hot encoded
-        check(samples, torch.argmax(rsamples, dim=-1))
-
     def test_diag_gaussian(self):
         batch_size = 128
         ndim = 4