[RLlib] Update autoregressive actions example. (ray-project#47829)

Signed-off-by: ujjawal-khare <ujjawal.khare@dream11.com>

rllib/BUILD

@@ -3260,7 +3260,7 @@ 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

@@ -147,7 +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.

rllib/examples/rl_modules/classes/autoregressive_actions_rlm.py

@@ -1,10 +1,11 @@
 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
@@ -233,10 +234,11 @@ def _forward_train(self, batch: Dict[str, TensorType]) -> Dict[str, TensorType]:
         return outs
 
     @override(ValueFunctionAPI)
-    def compute_values(self, batch: Dict[str, TensorType], embeddings=None):
-        # Encoder forward pass to get `embeddings`, if necessary.
-        if embeddings is None:
-            embeddings = self.encoder(batch)[ENCODER_OUT]
+    def compute_values(self, batch: Dict[str, TensorType]):
+
+        # Encoder forward pass.
+        encoder_outs = self.encoder(batch)[ENCODER_OUT]
+
         # Value head forward pass.
         vf_out = self.vf(embeddings)
         # Squeeze out last dimension (single node value head).