class_docstr: Environment for RL
class_docstr_tok: ["Environment","for","RL"]
code: def step(self, policy: RLPolicy):
      a_dist = policy(self.current_state)
      a = a_dist.sample()[0]
      s_dist = self(self.current_state, a)
      srs = []
      probs = []
      for s, rp in s_dist.items():
          srs.append(StateReward(s, rp.reward))
          probs.append(rp.prob)
      sr = random.choices(srs, weights=probs)[0]
  
      last_state = self.current_state
      noop = sr.state == self.current_state
      if not noop:
          self.current_state = sr.state
      self._steps_taken += 1
  
      status = Transition.Status.NORMAL
      if (
          0 < self._max_horizon <= self._steps_taken
          or self.current_state.is_terminal
      ):
          status = Transition.Status.TERMINATED
      elif noop:
          status = Transition.Status.NOOP
      return Transition(
          last_state=last_state,
          action=a,
          action_prob=a_dist[a],
          state=self.current_state,
          reward=sr.reward,
          status=status,
      )
  
file_path: ReAgent/reagent/ope/test/envs.py
imports: import random
  from abc import abstractmethod
  from typing import Optional
  from reagent.ope.estimators.sequential_estimators import Mdp, Model, RLPolicy, State, StateReward, Transition
label_desc: Use image node_id 4 for calling the Environment obj's underlying member method code with example usage: obj.step(policy) and returns: Transition