text: from rlf.policies.base_policy import BasePolicy, create_simple_action_data
  import numpy as np
  import torch
  from abc import ABC, abstractmethod
  from collections import defaultdict
  
  class SolvePolicy(BasePolicy, ABC):
      """
      Plan all actions ahead of time based on the starting state. This can be
      useful for classical planning algorithms such as path planning algorithms.
      """
  
      def __init__(self):
          super().__init__()
          self.is_first = True
  
      def init(self, obs_space, action_space, args):
          super().init(obs_space, action_space, args)
          self.all_actions = defaultdict(list)
          self.ep_idx = defaultdict(lambda: 0)
  
      @abstractmethod
      def _solve_env(self, state):
          pass
  
      def get_action(self, state, hxs, masks, step_info):
          if self.is_first:
              masks = torch.zeros(masks.shape)
  
          sel_actions = []
          for i, mask in enumerate(masks):
              if mask == 0.0:
                  if len(self.all_actions[i]) != 0:
                      # We should have exhaused all of the actions
                      assert self.ep_idx[i] == len(self.all_actions[i]) - 1
                  # The env has reset, solve it.
                  actions = self._solve_env(state[i])
                  self.all_actions[i] = actions
                  self.ep_idx[i] = 0
              else:
                  self.ep_idx[i] += 1
              sel_actions.append(self.all_actions[i][self.ep_idx[i]])
          sel_actions = torch.tensor(sel_actions).unsqueeze(-1)
          self.is_first = False
          return create_simple_action_data(sel_actions)
  
  