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Successfully trained a model (main/trained_models/) that crushes the final round of Street Fighter II Special Champion Edition.
69 lines
1.9 KiB
Python
69 lines
1.9 KiB
Python
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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RESET_ROUND = False # Reset the round when fight is over.
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RENDERING = True
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RANDOM_ACTION = False
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MODEL_PATH = r"trained_models/ppo_ryu_7000000_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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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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if not RANDOM_ACTION:
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# model.load(MODEL_PATH)
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model = PPO.load(MODEL_PATH, env=env)
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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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num_victory = 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 not done:
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timestamp = time.time()
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if RANDOM_ACTION:
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obs, reward, done, info = env.step(env.action_space.sample())
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else:
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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: {:.3f}, playerHP: {}, enemyHP:{}".format(reward, info['agent_hp'], info['enemy_hp']))
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if info['enemy_hp'] < 0:
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print("Victory!")
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num_victory += 1
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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("Winning rate: {}".format(1.0 * num_victory / num_episodes))
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if RANDOM_ACTION:
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print("Average reward for random action: {}".format(episode_reward_sum/num_episodes))
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else:
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print("Average reward for {}: {}".format(MODEL_PATH, episode_reward_sum/num_episodes)) |