code: import gym
  from gym.spaces import Box, Dict, Discrete
  import numpy as np
  import random
  
  
  class ParametricActionsCartPole(gym.Env):
      """Parametric action version of CartPole.
  
      In this env there are only ever two valid actions, but we pretend there are
      actually up to `max_avail_actions` actions that can be taken, and the two
      valid actions are randomly hidden among this set.
  
      At each step, we emit a dict of:
          - the actual cart observation
          - a mask of valid actions (e.g., [0, 0, 1, 0, 0, 1] for 6 max avail)
          - the list of action embeddings (w/ zeroes for invalid actions) (e.g.,
              [[0, 0],
               [0, 0],
               [-0.2322, -0.2569],
               [0, 0],
               [0, 0],
               [0.7878, 1.2297]] for max_avail_actions=6)
  
      In a real environment, the actions embeddings would be larger than two
      units of course, and also there would be a variable number of valid actions
      per step instead of always [LEFT, RIGHT].
      """
  
      def __init__(self, max_avail_actions):
          # Use simple random 2-unit action embeddings for [LEFT, RIGHT]
          self.left_action_embed = np.random.randn(2)
          self.right_action_embed = np.random.randn(2)
          self.action_space = Discrete(max_avail_actions)
          self.wrapped = gym.make("CartPole-v0")
          self.observation_space = Dict({
              "action_mask": Box(
                  0, 1, shape=(max_avail_actions, ), dtype=np.float32),
              "avail_actions": Box(-10, 10, shape=(max_avail_actions, 2)),
              "cart": self.wrapped.observation_space,
          })
  
      def update_avail_actions(self):
          self.action_assignments = np.array(
              [[0., 0.]] * self.action_space.n, dtype=np.float32)
          self.action_mask = np.array(
              [0.] * self.action_space.n, dtype=np.float32)
          self.left_idx, self.right_idx = random.sample(
              range(self.action_space.n), 2)
          self.action_assignments[self.left_idx] = self.left_action_embed
          self.action_assignments[self.right_idx] = self.right_action_embed
          self.action_mask[self.left_idx] = 1
          self.action_mask[self.right_idx] = 1
  
      def reset(self):
          self.update_avail_actions()
          return {
              "action_mask": self.action_mask,
              "avail_actions": self.action_assignments,
              "cart": self.wrapped.reset(),
          }
  
      def step(self, action):
          if action == self.left_idx:
              actual_action = 0
          elif action == self.right_idx:
              actual_action = 1
          else:
              raise ValueError(
                  "Chosen action was not one of the non-zero action embeddings",
                  action, self.action_assignments, self.action_mask,
                  self.left_idx, self.right_idx)
          orig_obs, rew, done, info = self.wrapped.step(actual_action)
          self.update_avail_actions()
          self.action_mask = self.action_mask.astype(np.float32)
          obs = {
              "action_mask": self.action_mask,
              "avail_actions": self.action_assignments,
              "cart": orig_obs,
          }
          return obs, rew, done, info
  
  
  class ParametricActionsCartPoleNoEmbeddings(gym.Env):
      """Same as the above ParametricActionsCartPole.
  
      However, action embeddings are not published inside observations,
      but will be learnt by the model.
  
      At each step, we emit a dict of:
          - the actual cart observation
          - a mask of valid actions (e.g., [0, 0, 1, 0, 0, 1] for 6 max avail)
          - action embeddings (w/ "dummy embedding" for invalid actions) are
            outsourced in the model and will be learned.
      """
  
      def __init__(self, max_avail_actions):
          # Randomly set which two actions are valid and available.
          self.left_idx, self.right_idx = random.sample(
              range(max_avail_actions), 2)
          self.valid_avail_actions_mask = np.array(
              [0.] * max_avail_actions, dtype=np.float32)
          self.valid_avail_actions_mask[self.left_idx] = 1
          self.valid_avail_actions_mask[self.right_idx] = 1
          self.action_space = Discrete(max_avail_actions)
          self.wrapped = gym.make("CartPole-v0")
          self.observation_space = Dict({
              "valid_avail_actions_mask": Box(0, 1, shape=(max_avail_actions, )),
              "cart": self.wrapped.observation_space,
          })
  
      def reset(self):
          return {
              "valid_avail_actions_mask": self.valid_avail_actions_mask,
              "cart": self.wrapped.reset(),
          }
  
      def step(self, action):
          if action == self.left_idx:
              actual_action = 0
          elif action == self.right_idx:
              actual_action = 1
          else:
              raise ValueError(
                  "Chosen action was not one of the non-zero action embeddings",
                  action, self.valid_avail_actions_mask, self.left_idx,
                  self.right_idx)
          orig_obs, rew, done, info = self.wrapped.step(actual_action)
          obs = {
              "valid_avail_actions_mask": self.valid_avail_actions_mask,
              "cart": orig_obs,
          }
          return obs, rew, done, info
  
apis: numpy.array numpy.random.randn