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Successfully trained a model (main/trained_models/) that crushes the final round of Street Fighter II Special Champion Edition.
35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
import retro
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from stable_baselines3 import PPO
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from stable_baselines3.common.vec_env import DummyVecEnv
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from stable_baselines3.common.monitor import Monitor
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from stable_baselines3.common.evaluation import evaluate_policy
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from street_fighter_custom_wrapper import StreetFighterCustomWrapper
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RESET_ROUND = True # Reset the round when fight is over.
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RENDERING = False
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MODEL_PATH = r"trained_models/ppo_ryu_2000000_steps"
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def make_env(game, state):
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def _init():
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env = retro.make(
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game=game,
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state=state,
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use_restricted_actions=retro.Actions.FILTERED,
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obs_type=retro.Observations.IMAGE
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)
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env = StreetFighterCustomWrapper(env, reset_round=RESET_ROUND, rendering=RENDERING)
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env = Monitor(env)
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return env
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return _init
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game = "StreetFighterIISpecialChampionEdition-Genesis"
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env = make_env(game, state="Champion.Level12.RyuVsBison")()
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model = PPO("CnnPolicy", env)
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model.load(MODEL_PATH)
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mean_reward, std_reward = evaluate_policy(model, env, render=False, n_eval_episodes=5, deterministic=False, return_episode_rewards=True)
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print(mean_reward)
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print(std_reward)
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# print(f"Reward: {mean_reward:.2f} +/- {std_reward:.2f}")
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