[RLlib] Update autoregressive actions example.

rllib/BUILD

@@ -3258,7 +3258,7 @@ py_test(
     tags = ["team:rllib", "exclusive", "examples"],
     size = "medium",
     srcs = ["examples/autoregressive_action_dist.py"],
-    args = ["--as-test", "--framework=tf", "--stop-reward=150", "--num-cpus=4"]
+    args = ["--as-test", "--framework=tf", "--stop-reward=-0.012", "--num-cpus=4"]
 )
 
 #@OldAPIStack
@@ -3268,7 +3268,7 @@ py_test(
     tags = ["team:rllib", "exclusive", "examples"],
     size = "medium",
     srcs = ["examples/autoregressive_action_dist.py"],
-    args = ["--as-test", "--framework=torch", "--stop-reward=150", "--num-cpus=4"]
+    args = ["--as-test", "--framework=torch", "--stop-reward=-0.012", "--num-cpus=4"]
 )
 
 #@OldAPIStack

rllib/examples/autoregressive_action_dist.py

@@ -41,7 +41,9 @@
 import ray
 from ray import air, tune
 from ray.air.constants import TRAINING_ITERATION
-from ray.rllib.examples.envs.classes.correlated_actions_env import CorrelatedActionsEnv
+from ray.rllib.examples.envs.classes.correlated_actions_env import (
+    AutoRegressiveActionEnv,
+)
 from ray.rllib.examples._old_api_stack.models.autoregressive_action_model import (
     AutoregressiveActionModel,
     TorchAutoregressiveActionModel,
@@ -102,7 +104,7 @@ def get_cli_args():
     parser.add_argument(
         "--stop-reward",
         type=float,
-        default=200.0,
+        default=-0.012,
         help="Reward at which we stop training.",
     )
     parser.add_argument(
@@ -145,7 +147,9 @@ def get_cli_args():
     config = (
         get_trainable_cls(args.run)
         .get_default_config()
-        .environment(CorrelatedActionsEnv)
+        # Batch-norm models have not been migrated to the RL Module API yet.
+        .api_stack(enable_rl_module_and_learner=False)
+        .environment(AutoRegressiveActionEnv)
         .framework(args.framework)
         .training(gamma=0.5)
         # Use GPUs iff `RLLIB_NUM_GPUS` env var set to > 0.
@@ -189,7 +193,7 @@ def get_cli_args():
 
         # run manual test loop: 1 iteration until done
         print("Finished training. Running manual test/inference loop.")
-        env = CorrelatedActionsEnv(_)
+        env = AutoRegressiveActionEnv(_)
         obs, info = env.reset()
         done = False
         total_reward = 0

rllib/examples/envs/classes/correlated_actions_env.py

@@ -1,44 +1,74 @@
 import gymnasium as gym
 from gymnasium.spaces import Box, Discrete, Tuple
 import numpy as np
-import random
+from typing import Any, Dict, Optional
 
 
-class CorrelatedActionsEnv(gym.Env):
-    """
-    Simple env in which the policy has to emit a tuple of equal actions.
+class AutoRegressiveActionEnv(gym.Env):
+    """Custom Environment with autoregressive continuous actions.
+
+    Simple env in which the policy has to emit a tuple of correlated actions.
 
-    In each step, the agent observes a random number (0 or 1) and has to choose
-    two actions a1 and a2.
-    It gets +5 reward for matching a1 to the random obs and +5 for matching a2
-    to a1. I.e., +10 at most per step.
+    In each step, the agent observes a random number (between -1 and 1) and has
+    to choose two actions a1 and a2.
+
+    It gets 0 reward for matching a2 to the random obs times action a1. In all
+    other cases the negative deviance between the desired action a2 and its
+    actual counterpart serves as reward. The reward is constructed in such a
+    way that actions need to be correlated to succeed. It is not possible
+    for the network to learn each action head separately.
 
     One way to effectively learn this is through correlated action
     distributions, e.g., in examples/rl_modules/autoregressive_action_rlm.py
 
-    There are 20 steps. Hence, the best score would be ~200 reward.
+    The game ends after the first step.
     """
 
     def __init__(self, _=None):
-        self.observation_space = Box(0, 1, shape=(1,), dtype=np.float32)
+
+        # Define the action space (two continuous actions a1, a2)
         self.action_space = Tuple([Discrete(2), Discrete(2)])
-        self.last_observation = None
 
-    def reset(self, *, seed=None, options=None):
-        self.t = 0
-        self.last_observation = np.array([random.choice([0, 1])], dtype=np.float32)
-        return self.last_observation, {}
+        # Define the observation space (state is a single continuous value)
+        self.observation_space = Box(low=-1, high=1, shape=(1,), dtype=np.float32)
+
+        # Internal state for the environment (e.g., could represent a factor
+        # influencing the relationship)
+        self.state = None
+
+    def reset(
+        self, seed: Optional[int] = None, options: Optional[Dict[str, Any]] = None
+    ):
+        """Reset the environment to an initial state."""
+        super().reset(seed=seed, options=options)
+
+        # Randomly initialize the state between -1 and 1
+        self.state = np.random.uniform(-1, 1, size=(1,))
+
+        return self.state, {}
 
     def step(self, action):
-        self.t += 1
+        """Apply the autoregressive action and return step information."""
+
+        # Extract actions
         a1, a2 = action
-        reward = 0
-        # Encourage correlation between most recent observation and a1.
-        if a1 == self.last_observation:
-            reward += 5
-        # Encourage correlation between a1 and a2.
-        if a1 == a2:
-            reward += 5
-        done = truncated = self.t > 20
-        self.last_observation = np.array([random.choice([0, 1])], dtype=np.float32)
-        return self.last_observation, reward, done, truncated, {}
+
+        # The state determines the desired relationship between a1 and a2
+        desired_a2 = (
+            self.state[0] * a1
+        )  # Autoregressive relationship dependent on state
+
+        # Reward is based on how close a2 is to the state-dependent autoregressive
+        # relationship
+        reward = -np.abs(a2 - desired_a2)  # Negative absolute error as the reward
+
+        # Optionally: add some noise or complexity to the reward function
+        # reward += np.random.normal(0, 0.01)  # Small noise can be added
+
+        # Terminate after each step (no episode length in this simple example)
+        done = True
+
+        # Empty info dictionary
+        info = {}
+
+        return self.state, reward, done, False, info

rllib/examples/rl_modules/autoregressive_actions_rl_module.py

@@ -10,7 +10,7 @@
     - Uses this `RLModule` in a PPO training run on a simple environment
         that rewards synchronized actions.
     - Stops the training after 100k steps or when the mean episode return
-        exceeds 150 in evaluation, i.e. if the agent has learned to
+        exceeds 0.01 in evaluation, i.e. if the agent has learned to
         synchronize its actions.
 
 How to run this script
@@ -42,7 +42,9 @@
 from ray.rllib.algorithms.ppo import PPOConfig
 from ray.rllib.core.models.catalog import Catalog
 from ray.rllib.core.rl_module.rl_module import RLModuleSpec
-from ray.rllib.examples.envs.classes.correlated_actions_env import CorrelatedActionsEnv
+from ray.rllib.examples.envs.classes.correlated_actions_env import (
+    AutoRegressiveActionEnv,
+)
 from ray.rllib.examples.rl_modules.classes.autoregressive_actions_rlm import (
     AutoregressiveActionsTorchRLM,
 )
@@ -59,7 +61,7 @@
 from ray.tune import register_env
 
 
-register_env("correlated_actions_env", lambda _: CorrelatedActionsEnv(_))
+register_env("correlated_actions_env", lambda _: AutoRegressiveActionEnv(_))
 
 parser = add_rllib_example_script_args(
     default_iters=200,
@@ -100,7 +102,7 @@
     # exceeds 150 in evaluation.
     stop = {
         f"{NUM_ENV_STEPS_SAMPLED_LIFETIME}": 100000,
-        f"{EVALUATION_RESULTS}/{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": 150.0,
+        f"{EVALUATION_RESULTS}/{ENV_RUNNER_RESULTS}/{EPISODE_RETURN_MEAN}": -0.012,
     }
 
     # Run the example (with Tune).

rllib/examples/rl_modules/classes/autoregressive_actions_rlm.py

@@ -1,25 +1,26 @@
+import abc
 from abc import abstractmethod
-from typing import Any, Dict
+from typing import Dict
 
 from ray.rllib.core import Columns
 from ray.rllib.core.models.base import ENCODER_OUT
 from ray.rllib.core.models.configs import MLPHeadConfig
 from ray.rllib.core.models.specs.specs_dict import SpecDict
+from ray.rllib.core.rl_module.apis.value_function_api import ValueFunctionAPI
 from ray.rllib.core.rl_module.rl_module import RLModule
 from ray.rllib.core.rl_module.torch.torch_rl_module import TorchRLModule
 from ray.rllib.utils.annotations import (
     override,
     OverrideToImplementCustomLogic_CallToSuperRecommended,
 )
 from ray.rllib.utils.framework import try_import_torch
-from ray.rllib.utils.torch_utils import convert_to_torch_tensor
 from ray.rllib.utils.typing import TensorType
 
 torch, nn = try_import_torch()
 
 
 # TODO (simon): Improvements: `inference-only` mode.
-class AutoregressiveActionsRLM(RLModule):
+class AutoregressiveActionsRLM(RLModule, ValueFunctionAPI, abc.ABC):
     """An RLModule that implements an autoregressive action distribution.
 
     This RLModule implements an autoregressive action distribution, where the
@@ -124,22 +125,6 @@ def pi(self, batch: Dict[str, TensorType]) -> Dict[str, TensorType]:
             A dict mapping Column names to batches of policy outputs.
         """
 
-    @abstractmethod
-    def _compute_values(self, batch) -> Any:
-        """Computes values using the vf-specific network(s) and given a batch of data.
-
-        Args:
-            batch: The input batch to pass through this RLModule (value function
-                encoder and vf-head).
-
-        Returns:
-            A dict mapping ModuleIDs to batches of value function outputs (already
-            squeezed on the last dimension (which should have shape (1,) b/c of the
-            single value output node). However, for complex multi-agent settings with
-            shareed value networks, the output might look differently (e.g. a single
-            return batch without the ModuleID-based mapping).
-        """
-
 
 class AutoregressiveActionsTorchRLM(TorchRLModule, AutoregressiveActionsRLM):
     @override(AutoregressiveActionsRLM)
@@ -261,12 +246,8 @@ def _forward_train(self, batch: Dict[str, TensorType]) -> Dict[str, TensorType]:
 
         return outs
 
-    @override(AutoregressiveActionsRLM)
-    def _compute_values(self, batch, device=None):
-        infos = batch.pop(Columns.INFOS, None)
-        batch = convert_to_torch_tensor(batch, device=device)
-        if infos is not None:
-            batch[Columns.INFOS] = infos
+    @override(ValueFunctionAPI)
+    def compute_values(self, batch: Dict[str, TensorType]):
 
         # Encoder forward pass.
         encoder_outs = self.encoder(batch)[ENCODER_OUT]