mirror of
https://github.com/linyiLYi/street-fighter-ai.git
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76 lines
2.2 KiB
Python
76 lines
2.2 KiB
Python
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import time
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import retro
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from stable_baselines3 import PPO
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from street_fighter_custom_wrapper import StreetFighterCustomWrapper
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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)
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return env
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return _init
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game = "StreetFighterIISpecialChampionEdition-Genesis"
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state_stages = [
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"Champion.Level1.RyuVsGuile",
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"Champion.Level1.ChunLiVsGuile", # Average reward for random strategy: -102.3 | -20.4
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"ChampionX.Level1.ChunLiVsKen", # Average reward for random strategy: -247.6
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"Champion.Level2.ChunLiVsKen",
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"Champion.Level3.ChunLiVsChunLi",
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"Champion.Level4.ChunLiVsZangief",
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"Champion.Level5.ChunLiVsDhalsim",
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"Champion.Level6.ChunLiVsRyu",
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"Champion.Level7.ChunLiVsEHonda",
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"Champion.Level8.ChunLiVsBlanka",
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"Champion.Level9.ChunLiVsBalrog",
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"Champion.Level10.ChunLiVsVega",
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"Champion.Level11.ChunLiVsSagat",
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"Champion.Level12.ChunLiVsBison"
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# Add other stages as necessary
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]
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env = make_env(game, state_stages[0])()
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model = PPO(
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"CnnPolicy",
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env,
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verbose=1
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)
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model_path = r"trained_models_ryu_level_1_reward_x3/ppo_ryu_6600000_steps"
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model.load(model_path)
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# Average reward for optuna/trial_1_best_model: -82.3
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# Average reward for optuna/trial_9_best_model: 36.7 | -86.23
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# Average reward for trained_models/ppo_chunli_5376000_steps: -77.8
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obs = env.reset()
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done = False
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num_episodes = 30
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episode_reward_sum = 0
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for _ in range(num_episodes):
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done = False
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obs = env.reset()
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total_reward = 0
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while True:
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timestamp = time.time()
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action, _states = model.predict(obs)
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obs, reward, done, info = env.step(action)
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if reward != 0:
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total_reward += reward
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print("Reward: {}, playerHP: {}, enemyHP:{}".format(reward, info['health'], info['enemy_health']))
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env.render()
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# time.sleep(0.005)
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# print("Total reward: {}".format(total_reward))
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# episode_reward_sum += total_reward
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# env.close()
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# print("Average reward for {}: {}".format(model_path, episode_reward_sum/num_episodes))
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