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11 months ago
11 months ago
  1. from typing import Dict, Optional
  2. from ray.rllib.env.env_context import EnvContext
  3. from ray.rllib.policy import Policy
  4. from ray.rllib.utils.typing import EnvType, PolicyID
  5. from ray.rllib.algorithms.algorithm import Algorithm
  6. from ray.rllib.env.base_env import BaseEnv
  7. from ray.rllib.evaluation import RolloutWorker
  8. from ray.rllib.evaluation.episode import Episode
  9. from ray.rllib.evaluation.episode_v2 import EpisodeV2
  10. from ray.rllib.algorithms.callbacks import DefaultCallbacks, make_multi_callbacks
  11. import matplotlib.pyplot as plt
  12. class CustomCallback(DefaultCallbacks):
  13. def on_episode_start(self, *, worker: RolloutWorker, base_env: BaseEnv, policies: Dict[PolicyID, Policy], episode, env_index, **kwargs) -> None:
  14. env = base_env.get_sub_environments()[0]
  15. episode.user_data["count"] = 0
  16. episode.user_data["ran_into_lava"] = []
  17. episode.user_data["goals_reached"] = []
  18. episode.user_data["ran_into_adversary"] = []
  19. episode.hist_data["ran_into_lava"] = []
  20. episode.hist_data["goals_reached"] = []
  21. episode.hist_data["ran_into_adversary"] = []
  22. def on_episode_step(self, *, worker: RolloutWorker, base_env: BaseEnv, policies, episode, env_index, **kwargs) -> None:
  23. episode.user_data["count"] = episode.user_data["count"] + 1
  24. env = base_env.get_sub_environments()[0]
  25. def on_episode_end(self, *, worker: RolloutWorker, base_env: BaseEnv, policies, episode, env_index, **kwargs) -> None:
  26. # print(F"Epsiode end Environment: {base_env.get_sub_environments()}")
  27. env = base_env.get_sub_environments()[0]
  28. agent_tile = env.grid.get(env.agent_pos[0], env.agent_pos[1])
  29. ran_into_adversary = False
  30. if hasattr(env, "adversaries"):
  31. adversaries = env.adversaries.values()
  32. for adversary in adversaries:
  33. if adversary.cur_pos[0] == env.agent_pos[0] and adversary.cur_pos[1] == env.agent_pos[1]:
  34. ran_into_adversary = True
  35. break
  36. episode.user_data["goals_reached"].append(agent_tile is not None and agent_tile.type == "goal")
  37. episode.user_data["ran_into_lava"].append(agent_tile is not None and agent_tile.type == "lava")
  38. episode.user_data["ran_into_adversary"].append(ran_into_adversary)
  39. episode.custom_metrics["reached_goal"] = agent_tile is not None and agent_tile.type == "goal"
  40. episode.custom_metrics["ran_into_lava"] = agent_tile is not None and agent_tile.type == "lava"
  41. episode.custom_metrics["ran_into_adversary"] = ran_into_adversary
  42. #print("On episode end print")
  43. # print(env.printGrid())
  44. episode.hist_data["goals_reached"] = episode.user_data["goals_reached"]
  45. episode.hist_data["ran_into_lava"] = episode.user_data["ran_into_lava"]
  46. episode.hist_data["ran_into_adversary"] = episode.user_data["ran_into_adversary"]
  47. def on_evaluate_start(self, *, algorithm: Algorithm, **kwargs) -> None:
  48. print("Evaluate Start")
  49. def on_evaluate_end(self, *, algorithm: Algorithm, evaluation_metrics: dict, **kwargs) -> None:
  50. print("Evaluate End")