requirements.txt

main/requirements.txt

@@ -0,0 +1,5 @@
+gym==0.18.3
+gym-retro==0.8.0
+opencv-python==4.7.0.72
+stable-baselines3==1.1.0
+tensorboard==2.12.1

main/tune_ppo.py

@@ -1,68 +0,0 @@
-import os
-
-import retro
-import optuna
-from stable_baselines3 import PPO
-from stable_baselines3.common.monitor import Monitor
-from stable_baselines3.common.evaluation import evaluate_policy
-
-from street_fighter_custom_wrapper import StreetFighterCustomWrapper
-
-LOG_DIR = 'logs/'
-OPT_DIR = 'optuna/'
-os.makedirs(LOG_DIR, exist_ok=True)
-os.makedirs(OPT_DIR, exist_ok=True)
-
-def optimize_ppo(trial): 
-    return {
-        'n_steps':trial.suggest_int('n_steps', 512, 2048, log=True),
-        'gamma':trial.suggest_float('gamma', 0.9, 0.9999),
-        'learning_rate':trial.suggest_float('learning_rate', 5e-5, 5e-4, log=True),
-        'gae_lambda':trial.suggest_float('gae_lambda', 0.8, 0.9999)
-    }
-
-def make_env(game, state):
-    def _init():
-        env = retro.make(
-            game=game, 
-            state=state, 
-            use_restricted_actions=retro.Actions.FILTERED, 
-            obs_type=retro.Observations.IMAGE
-        )
-        env = StreetFighterCustomWrapper(env)
-        return env
-    return _init
-
-def optimize_agent(trial):
-    game = "StreetFighterIISpecialChampionEdition-Genesis"
-    state = "Champion.Level1.ChunLiVsGuile"#"ChampionX.Level1.ChunLiVsKen"
-
-    try:
-        model_params = optimize_ppo(trial) 
-
-        # Create environment 
-        env = make_env(game, state)()
-        env = Monitor(env, LOG_DIR)
-
-        # Create algo 
-        model = PPO('CnnPolicy', env, verbose=1, **model_params)
-        model.learn(total_timesteps=500000)
-
-        # Evaluate model 
-        mean_reward, _ = evaluate_policy(model, env, n_eval_episodes=30, deterministic=False)
-        env.close()
-
-        SAVE_PATH = os.path.join(OPT_DIR, 'trial_{}_best_model'.format(trial.number))
-        model.save(SAVE_PATH)
-
-        return mean_reward
-    
-    except Exception as e:
-        return -1
-    
-# Creating the experiment 
-study = optuna.create_study(direction='maximize')
-study.optimize(optimize_agent, n_trials=10, n_jobs=1)
-
-print(study.best_params)
-print(study.best_trial)