class_docstr: Optimal RLlib policy for the CliffWalkingWallEnv environment, defined in
  ray/rllib/examples/env/cliff_walking_wall_env.py, with epsilon-greedy exploration.
  
  The policy takes a random action with probability epsilon, specified
  by `config["epsilon"]`, and the optimal action with probability  1 - epsilon.
class_docstr_tok: ["Optimal","RLlib","policy","for","the","CliffWalkingWallEnv","environment",",","defined","in","ray\/rllib\/examples\/env\/cliff_walking_wall_env.py",",","with","epsilon-greedy","exploration",".","The","policy","takes","a","random","action","with","probability","epsilon",",","specified","by","`","config","[","``","epsilon","''","]","`",",","and","the","optimal","action","with","probability","1","-","epsilon","."]
code: def compute_log_likelihoods(
      self,
      actions: Union[List[TensorType], TensorType],
      obs_batch: Union[List[TensorType], TensorType],
      **kwargs,
  ) -> TensorType:
      obs = np.array(obs_batch, dtype=int)
      actions = np.array(actions, dtype=int)
      # Compute action probs for all possible actions
      action_probs = self.action_dist[obs]
      # Take the action_probs corresponding to the specified actions
      action_probs = action_probs[np.arange(len(obs)), actions]
      # Ignore RuntimeWarning thrown by np.log(0) if action_probs is 0
      with np.errstate(divide="ignore"):
          return np.log(action_probs)
  
file_path: ray/python/ray/rllib/examples/policy/cliff_walking_wall_policy.py
imports: import gymnasium
  from typing import Dict, Union, List, Tuple, Optional
  import numpy
  from ray.rllib.policy.policy import Policy, ViewRequirement
  from ray.rllib.policy.sample_batch import SampleBatch
  from ray.rllib.models.torch.torch_action_dist import TorchCategorical
  from ray.rllib.utils.typing import AlgorithmConfigDict, TensorStructType, TensorType
  from ray.rllib.utils.annotations import override
  from ray.rllib.utils.debug import update_global_seed_if_necessary
label_desc: Use image node_id 3 for calling the CliffWalkingWallPolicy obj's underlying member method code with example usage: obj.compute_log_likelihoods(actions, obs_batch) and returns: np