more frames (enlarge time scope)

004_rgb_stack_ram_based_reward_custom/street_fighter_custom_wrapper.py

@@ -9,6 +9,21 @@ class StreetFighterCustomWrapper(gym.Wrapper):
     def __init__(self, env, testing=False):
         super(StreetFighterCustomWrapper, self).__init__(env)
         self.env = env
+        self.state_stages = [
+            "Champion.Level1.RyuVsGuile",
+            "Champion.Level2.RyuVsKen",
+            "Champion.Level3.RyuVsChunLi",
+            "Champion.Level4.RyuVsZangief",
+            "Champion.Level5.RyuVsDhalsim",
+            "Champion.Level6.RyuVsRyu",
+            "Champion.Level7.RyuVsEHonda",
+            "Champion.Level8.RyuVsBlanka",
+            "Champion.Level9.RyuVsBalrog",
+            "Champion.Level10.RyuVsVega",
+            "Champion.Level11.RyuVsSagat",
+            "Champion.Level12.RyuVsBison"
+        ]
+        self.current_stage = 0
 
         # Use a deque to store the last 4 frames
         self.num_frames = 3
@@ -37,7 +52,10 @@ class StreetFighterCustomWrapper(gym.Wrapper):
         return stacked_image
 
     def reset(self):
+        self.env.unwrapped.load_state(self.state_stages[self.current_stage])
+        self.current_stage = (self.current_stage + 1) % len(self.state_stages)
         observation = self.env.reset()
+        
         self.prev_player_health = self.full_hp
         self.prev_oppont_health = self.full_hp
 
@@ -53,8 +71,8 @@ class StreetFighterCustomWrapper(gym.Wrapper):
     def step(self, action):
         
         obs, _reward, _done, info = self.env.step(action)
-        curr_player_health = info['health']
-        curr_oppont_health = info['enemy_health']
+        curr_player_health = info['agent_hp']
+        curr_oppont_health = info['enemy_hp']
         
         self.total_timesteps += 1
 

004_rgb_stack_ram_based_reward_custom/test.py

@@ -50,25 +50,28 @@ state_stages = [
 #     # Add other stages as necessary
 # ]
 
-env = make_env(game, state_stages[11])()
+env = make_env(game, state_stages[0])()
 
 model = PPO(
     "CnnPolicy", 
     env,
     verbose=1
 )
-model_path = r"trained_models_ryu_level_1_time_reward_small_random/ppo_ryu_2600000_steps"
+model_path = r"trained_models_ryu_level_1_time_reward_small_loop_continue/ppo_ryu_5000000_steps.zip"
 model.load(model_path)
 # Average reward for optuna/trial_1_best_model: -82.3
 # Average reward for optuna/trial_9_best_model: 36.7 | -86.23
 # Average reward for trained_models/ppo_chunli_5376000_steps: -77.8
 
+# Level_1 Average reward for trained_models_ryu_level_1_time_reward_small_random/ppo_ryu_4200000_steps: 0.35772262101207986 Winning rate: 0.5666666666666667
+# Level_2 Average reward for trained_models_ryu_level_1_time_reward_small_random/ppo_ryu_4200000_steps: 0.18094390738868166 Winning rate: 0.16666666666666666
 
-obs = env.reset()
+# obs = env.reset()
 done = False
 
-num_episodes = 30
+num_episodes = 12
 episode_reward_sum = 0
+num_victory = 0
 for _ in range(num_episodes):
     done = False
     obs = env.reset()
@@ -81,11 +84,15 @@ for _ in range(num_episodes):
 
         if reward != 0:
             total_reward += reward
-            print("Reward: {}, playerHP: {}, enemyHP:{}".format(reward, info['health'], info['enemy_health']))
+            print("Reward: {}, playerHP: {}, enemyHP:{}".format(reward, info['agent_hp'], info['enemy_hp']))
         env.render()
         # time.sleep(0.005)
+    if info['enemy_hp'] < 0:
+        print("Victory!")
+        num_victory += 1
     print("Total reward: {}".format(total_reward))
     episode_reward_sum += total_reward
 
-# env.close()
-# print("Average reward for {}: {}".format(model_path, episode_reward_sum/num_episodes))
+env.close()
+print("Winning rate: {}".format(1.0 * num_victory / num_episodes))
+print("Average reward for {}: {}".format(model_path, episode_reward_sum/num_episodes))

004_rgb_stack_ram_based_reward_custom/train.py

@@ -12,33 +12,32 @@ from street_fighter_custom_wrapper import StreetFighterCustomWrapper
 LOG_DIR = 'logs'
 os.makedirs(LOG_DIR, exist_ok=True)
 
-class RandomOpponentChangeCallback(BaseCallback):
-    def __init__(self, stages, opponent_interval, verbose=0):
-        super(RandomOpponentChangeCallback, self).__init__(verbose)
-        self.stages = stages
-        self.opponent_interval = opponent_interval
+# class RandomOpponentChangeCallback(BaseCallback):
+#     def __init__(self, stages, opponent_interval, verbose=0):
+#         super(RandomOpponentChangeCallback, self).__init__(verbose)
+#         self.stages = stages
+#         self.opponent_interval = opponent_interval
 
-    def _on_step(self) -> bool:
-        if self.n_calls % self.opponent_interval == 0:
-            new_state = random.choice(self.stages)
-            print("\nCurrent state:", new_state)
-            self.training_env.env_method("load_state", new_state, indices=None)
-        return True
+#     def _on_step(self) -> bool:
+#         if self.n_calls % self.opponent_interval == 0:
+#             new_state = random.choice(self.stages)
+#             print("\nCurrent state:", new_state)
+#             self.training_env.env_method("load_state", new_state, indices=None)
+#         return True
     
 # class StageIncreaseCallback(BaseCallback):
-#     def __init__(self, stages, stage_interval, save_dir, verbose=0):
+#     def __init__(self, stages, stage_interval, verbose=0):
 #         super(StageIncreaseCallback, self).__init__(verbose)
 #         self.stages = stages
 #         self.stage_interval = stage_interval
-#         self.save_dir = save_dir
 #         self.current_stage = 0
 
 #     def _on_step(self) -> bool:
-#         if self.n_calls % self.stage_interval == 0 and self.current_stage < len(self.stages) - 1:
+#         if self.n_calls % self.stage_interval == 0:
 #             self.current_stage += 1
-#             new_state = self.stages[self.current_stage]
+#             new_state = self.stages[self.current_stage % len(self.stages)]
+#             print("\nCurrent state:", new_state)
 #             self.training_env.env_method("load_state", new_state, indices=None)
-#             self.model.save(os.path.join(self.save_dir, f"ppo_chunli_stage_{self.current_stage}.zip"))
 #         return True
     
 def make_env(game, state):
@@ -116,30 +115,34 @@ def main():
         env,
         device="cuda", 
         verbose=1,
-        n_steps=1024,
-        batch_size=64,
-        learning_rate=1e-4,
+        n_steps=8192,
+        batch_size=128,
+        learning_rate=1e-5,
+        target_kl=0.03,
         tensorboard_log="logs"
     )
 
     # Set the save directory
-    save_dir = "trained_models_ryu_level_1_time_reward_small_random"
+    save_dir = "trained_models_ryu_level_1_time_reward_small_loop_continue"
     os.makedirs(save_dir, exist_ok=True)
 
     # Load the model from file
-    model_path = "trained_models_ryu_level_1_time_reward_small_continue/ppo_ryu_400000_steps.zip"
+    model_path = "trained_models_ryu_level_1_time_reward_small_loop/ppo_ryu_1200000_steps.zip"
     
     # Load model and modify the learning rate and entropy coefficient
-    # custom_objects = {
-    #     "learning_rate": 0.0002
-    # }
-    model = PPO.load(model_path, env=env, device="cuda")#, custom_objects=custom_objects)
+    custom_objects = {
+        "learning_rate": 1e-5,
+        "target_kl": 0.03,
+    }
+    model = PPO.load(model_path, env=env, device="cuda", custom_objects=custom_objects)
 
     # Set up callbacks
-    opponent_interval = 32768 # stage_interval * num_envs = total_steps_per_stage
+    # opponent_interval = 32768 # stage_interval * num_envs = total_steps_per_stage
+    # stage_interval = 400000
     checkpoint_interval = 200000 # checkpoint_interval * num_envs = total_steps_per_checkpoint (Every 80 rounds)
     checkpoint_callback = CheckpointCallback(save_freq=checkpoint_interval, save_path=save_dir, name_prefix="ppo_ryu")
-    stage_increase_callback = RandomOpponentChangeCallback(state_stages, opponent_interval, save_dir)
+    # stage_increase_callback = RandomOpponentChangeCallback(state_stages, opponent_interval)
+    # stage_increase_callback = StageIncreaseCallback(state_stages, stage_interval)
 
     # model_params = {
     #     'n_steps': 5, 
@@ -160,8 +163,8 @@ def main():
         sys.stdout = log_file
     
         model.learn(
-            total_timesteps=int(10000000), # total_timesteps = stage_interval * num_envs * num_stages (1120 rounds)
-            callback=[checkpoint_callback, stage_increase_callback]
+            total_timesteps=int(20000000), # total_timesteps = stage_interval * num_envs * num_stages (1120 rounds)
+            callback=[checkpoint_callback]#, stage_increase_callback]
         )
         env.close()
 

006_rgb_stack_ram_based_reward_time_reward_final_round/check_reward.py

@@ -0,0 +1,51 @@
+import os
+import time 
+
+import retro
+from stable_baselines3.common.monitor import Monitor
+
+from street_fighter_custom_wrapper import StreetFighterCustomWrapper
+    
+LOG_DIR = 'logs/'
+os.makedirs(LOG_DIR, exist_ok=True)
+
+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
+
+game = "StreetFighterIISpecialChampionEdition-Genesis"
+state = "Champion.Level1.RyuVsGuile"
+
+env = make_env(game, state)()
+env = Monitor(env, 'logs/')
+
+num_episodes = 30
+episode_reward_sum = 0
+for _ in range(num_episodes):
+    done = False
+    obs = env.reset()
+    total_reward = 0
+    while not done:
+        timestamp = time.time()
+        obs, reward, done, info = env.step(env.action_space.sample())
+
+        # Note that if player wins but only has 0 HP left, the winning reward is still 0, so it won't be printed. 
+        if reward != 0:
+            total_reward += reward
+            print("Reward: {}, playerHP: {}, enemyHP:{}".format(reward, info['health'], info['enemy_health']))
+        env.render()
+        # time.sleep(0.005)
+
+    print("Total reward: {}".format(total_reward))
+    episode_reward_sum += total_reward
+
+env.close()
+print("Average reward for random strategy: {}".format(episode_reward_sum/num_episodes))

006_rgb_stack_ram_based_reward_time_reward_final_round/custom_cnn.py

@@ -0,0 +1,24 @@
+import gym
+import torch
+import torch.nn as nn
+from stable_baselines3.common.torch_layers import BaseFeaturesExtractor
+
+# Custom feature extractor (CNN)
+class CustomCNN(BaseFeaturesExtractor):
+    def __init__(self, observation_space: gym.Space):
+        super(CustomCNN, self).__init__(observation_space, features_dim=512)
+        self.cnn = nn.Sequential(
+            nn.Conv2d(4, 32, kernel_size=5, stride=2, padding=0),
+            nn.ReLU(),
+            nn.Conv2d(32, 64, kernel_size=5, stride=2, padding=0),
+            nn.ReLU(),
+            nn.Conv2d(64, 64, kernel_size=3, stride=1, padding=0),
+            nn.ReLU(),
+            nn.Flatten(),
+            nn.Linear(16384, self.features_dim),
+            nn.ReLU()
+        )
+
+    def forward(self, observations: torch.Tensor) -> torch.Tensor:
+        return self.cnn(observations)
+    

006_rgb_stack_ram_based_reward_time_reward_final_round/evaluate.py

@@ -0,0 +1,52 @@
+import retro
+
+from stable_baselines3 import PPO
+from stable_baselines3.common.vec_env import DummyVecEnv
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.evaluation import evaluate_policy
+
+from custom_cnn import CustomCNN
+from street_fighter_custom_wrapper import StreetFighterCustomWrapper
+
+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
+
+game = "StreetFighterIISpecialChampionEdition-Genesis"
+state_stages = [
+    "Champion.Level1.ChunLiVsGuile",
+    "Champion.Level2.ChunLiVsKen",
+    "Champion.Level3.ChunLiVsChunLi",
+    "Champion.Level4.ChunLiVsZangief",
+    "Champion.Level5.ChunLiVsDhalsim",
+    "Champion.Level6.ChunLiVsRyu",
+    "Champion.Level7.ChunLiVsEHonda",
+    "Champion.Level8.ChunLiVsBlanka",
+    "Champion.Level9.ChunLiVsBalrog",
+    "Champion.Level10.ChunLiVsVega",
+    "Champion.Level11.ChunLiVsSagat",
+    "Champion.Level12.ChunLiVsBison"
+    # Add other stages as necessary
+]
+
+env = make_env(game, state_stages[0])()
+
+# Wrap the environment
+# env = Monitor(env, 'logs/')
+
+policy_kwargs = {'features_extractor_class': CustomCNN}
+model = PPO("CnnPolicy", env, policy_kwargs=policy_kwargs)
+
+model = PPO.load(r"dummy_model_ppo_chunli")
+# model.load(r"trained_models/ppo_chunli_864000_steps")
+
+mean_reward, std_reward = evaluate_policy(model, env, render=True, n_eval_episodes=10, deterministic=False, return_episode_rewards=True)
+print(f"Mean reward: {mean_reward:.2f} +/- {std_reward:.2f}")

006_rgb_stack_ram_based_reward_time_reward_final_round/logs/0/monitor.csv

@@ -0,0 +1,464 @@
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006_rgb_stack_ram_based_reward_time_reward_final_round/street_fighter_custom_wrapper.py

@@ -0,0 +1,92 @@
+import math
+import collections
+
+import gym
+import numpy as np
+
+# Custom environment wrapper
+class StreetFighterCustomWrapper(gym.Wrapper):
+    def __init__(self, env, testing=False):
+        super(StreetFighterCustomWrapper, self).__init__(env)
+        self.env = env
+
+        # Use a deque to store the last 4 frames
+        self.num_frames = 9
+        self.frame_stack = collections.deque(maxlen=self.num_frames)
+
+        self.reward_coeff = 1.0
+
+        self.total_timesteps = 0
+
+        self.full_hp = 176
+        self.prev_player_health = self.full_hp
+        self.prev_oppont_health = self.full_hp
+
+        # Update observation space to include stacked grayscale images
+        self.observation_space = gym.spaces.Box(low=0, high=255, shape=(100, 128, 3), dtype=np.uint8)
+        
+        self.testing = testing
+    
+    def _preprocess_observation(self, observation):
+
+        # Stack the downsampled frames.
+        self.frame_stack.append(observation[::2, ::2, :])
+
+        # Stack the R, G, B channel of each frame and return the "image".
+        # return np.stack([frame[:, :, i] for i, frame in enumerate(self.frame_stack)], axis=-1)
+        stacked_image = np.stack([self.frame_stack[i * 3 + 2][:, :, i] for i in range(3)], axis=-1)
+        return stacked_image
+
+    def reset(self):
+        observation = self.env.reset()
+        
+        self.prev_player_health = self.full_hp
+        self.prev_oppont_health = self.full_hp
+
+        self.total_timesteps = 0
+        
+        # Clear the frame stack and add the first observation [num_frames] times
+        self.frame_stack.clear()
+        for _ in range(self.num_frames):
+            self.frame_stack.append(observation[::2, ::2, :])
+
+        # return np.stack([frame[:, :, i] for i, frame in enumerate(self.frame_stack)], axis=-1)
+        return np.stack([self.frame_stack[i * 3 + 2][:, :, i] for i in range(3)], axis=-1)
+
+    def step(self, action):
+        
+        obs, _reward, _done, info = self.env.step(action)
+        curr_player_health = info['agent_hp']
+        curr_oppont_health = info['enemy_hp']
+        
+        self.total_timesteps += 1
+
+        # Game is over and player loses.
+        if curr_player_health < 0:
+            custom_reward = -math.pow(self.full_hp, (curr_oppont_health + 1) / (self.full_hp + 1))    # Use the remaining health points of opponent as penalty. 
+                                                   # If the opponent also has negative health points, it's a even game and the reward is +1.
+            custom_done = True
+
+        # Game is over and player wins.
+        elif curr_oppont_health < 0:
+            # custom_reward = curr_player_health * self.reward_coeff # Use the remaining health points of player as reward.
+                                                                   # Multiply by reward_coeff to make the reward larger than the penalty to avoid cowardice of agent.
+
+            # custom_reward = math.pow(self.full_hp, (5940 - self.total_timesteps) / 5940) * self.reward_coeff # Use the remaining time steps as reward.
+            custom_reward = math.pow(self.full_hp, (curr_player_health + 1) / (self.full_hp + 1)) * self.reward_coeff
+            custom_done = True
+
+        # While the fighting is still going on
+        else:
+            custom_reward = self.reward_coeff * (self.prev_oppont_health - curr_oppont_health) - (self.prev_player_health - curr_player_health)
+            self.prev_player_health = curr_player_health
+            self.prev_oppont_health = curr_oppont_health
+            custom_done = False
+
+        # During testing, the session should always keep going.
+        if self.testing:
+            custom_done = False
+             
+        # Max reward is 2 * full_hp = 352 (damage + winning_reward) 
+        return self._preprocess_observation(obs), 0.003 * custom_reward, custom_done, info # reward normalization
+    

006_rgb_stack_ram_based_reward_time_reward_final_round/test.py

@@ -0,0 +1,61 @@
+import time 
+
+import retro
+from stable_baselines3 import PPO
+
+from street_fighter_custom_wrapper import StreetFighterCustomWrapper
+    
+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
+
+game = "StreetFighterIISpecialChampionEdition-Genesis"
+
+env = make_env(game, state="Champion.Level12.RyuVsBison")()
+
+model = PPO(
+    "CnnPolicy", 
+    env,
+    verbose=1
+)
+model_path = r"trained_models_ryu_vs_bison_finetune/ppo_ryu_9500000_steps.zip"
+model.load(model_path)
+
+# obs = env.reset()
+done = False
+
+num_episodes = 100
+episode_reward_sum = 0
+num_victory = 0
+for _ in range(num_episodes):
+    done = False
+    obs = env.reset()
+    total_reward = 0
+    while not done:
+    # while True:
+        timestamp = time.time()
+        action, _states = model.predict(obs)
+        obs, reward, done, info = env.step(action)
+
+        if reward != 0:
+            total_reward += reward
+            print("Reward: {}, playerHP: {}, enemyHP:{}".format(reward, info['agent_hp'], info['enemy_hp']))
+        env.render()
+        # time.sleep(0.002)
+    if info['enemy_hp'] < 0:
+        print("Victory!")
+        num_victory += 1
+    print("Total reward: {}".format(total_reward))
+    episode_reward_sum += total_reward
+
+env.close()
+print("Winning rate: {}".format(1.0 * num_victory / num_episodes))
+print("Average reward for {}: {}".format(model_path, episode_reward_sum/num_episodes))

006_rgb_stack_ram_based_reward_time_reward_final_round/train.py

@@ -0,0 +1,116 @@
+import os
+import sys
+
+import retro
+from stable_baselines3 import PPO
+from stable_baselines3.common.monitor import Monitor
+from stable_baselines3.common.callbacks import CheckpointCallback
+from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv
+
+from street_fighter_custom_wrapper import StreetFighterCustomWrapper
+
+NUM_ENV = 16
+LOG_DIR = 'logs'
+os.makedirs(LOG_DIR, exist_ok=True)
+
+# Linear scheduler
+def linear_schedule(initial_value, final_value=0.0):
+
+    if isinstance(initial_value, str):
+        initial_value = float(initial_value)
+        final_value = float(final_value)
+        assert (initial_value > 0.0)
+
+    def scheduler(progress):
+        return final_value + progress * (initial_value - final_value)
+
+    return scheduler
+
+def make_env(game, state, seed=0):
+    def _init():
+        env = retro.make(
+            game=game, 
+            state=state, 
+            use_restricted_actions=retro.Actions.FILTERED, 
+            obs_type=retro.Observations.IMAGE    
+        )
+        env = StreetFighterCustomWrapper(env)
+        # Create log directory
+        env_log_dir = os.path.join(LOG_DIR, str(seed+100)) # +100 to avoid conflict with other log dirs when fine-tuning
+        os.makedirs(env_log_dir, exist_ok=True)
+        env = Monitor(env, env_log_dir)
+        env.seed(seed)
+        return env
+    return _init
+
+def main():
+    # Set up the environment and model
+    game = "StreetFighterIISpecialChampionEdition-Genesis"
+    env = SubprocVecEnv([make_env(game, state="Champion.Level12.RyuVsBison", seed=i) for i in range(NUM_ENV)])
+
+    # Set linear schedule for learning rate
+    # Start
+    lr_schedule = linear_schedule(2.5e-4, 2.5e-6)
+
+    # fine-tune
+    # lr_schedule = linear_schedule(5.0e-5, 2.5e-6)
+
+    # Set linear scheduler for clip range
+    # Start
+    clip_range_schedule = linear_schedule(0.15, 0.025)
+
+    # fine-tune
+    # clip_range_schedule = linear_schedule(0.075, 0.025)
+
+    model = PPO(
+        "CnnPolicy", 
+        env,
+        device="cuda", 
+        verbose=1,
+        n_steps=128,
+        batch_size=256,
+        n_epochs=4,
+        gamma=0.94,
+        learning_rate=lr_schedule,
+        clip_range=clip_range_schedule,
+        tensorboard_log="logs"
+    )
+
+    # Set the save directory
+    save_dir = "trained_models_ryu_vs_bison_no_coef_no_time_reward_9_frame"
+    os.makedirs(save_dir, exist_ok=True)
+
+    # Load the model from file
+    # model_path = "trained_models_ryu_vs_bison_finetune/ppo_ryu_10000000_steps.zip"
+    
+    # Load model and modify the learning rate and entropy coefficient
+    # custom_objects = {
+    #     "learning_rate": lr_schedule,
+    #     "clip_range": clip_range_schedule,
+    # }
+    # model = PPO.load(model_path, env=env, device="cuda", custom_objects=custom_objects)
+
+    # Set up callbacks
+    checkpoint_interval = 31250 # checkpoint_interval * num_envs = total_steps_per_checkpoint
+    checkpoint_callback = CheckpointCallback(save_freq=checkpoint_interval, save_path=save_dir, name_prefix="ppo_ryu")
+
+    # Writing the training logs from stdout to a file
+    original_stdout = sys.stdout
+    log_file_path = os.path.join(save_dir, "training_log.txt")
+    with open(log_file_path, 'w') as log_file:
+        sys.stdout = log_file
+    
+        model.learn(
+            total_timesteps=int(100000000), # total_timesteps = stage_interval * num_envs * num_stages (1120 rounds)
+            callback=[checkpoint_callback]#, stage_increase_callback]
+        )
+        env.close()
+
+    # Restore stdout
+    sys.stdout = original_stdout
+
+    # Save the final model
+    model.save(os.path.join(save_dir, "ppo_sf2_ryu_final.zip"))
+
+if __name__ == "__main__":
+    main()

006_rgb_stack_ram_based_reward_time_reward_final_round/trained_models_ryu_vs_bison_no_coef_no_time_reward_9_frame/training_log.txt

@@ -0,0 +1,941 @@
+Logging to logs\PPO_6
+-----------------------------
+| time/              |      |
+|    fps             | 646  |
+|    iterations      | 1    |
+|    time_elapsed    | 3    |
+|    total_timesteps | 2048 |
+-----------------------------
+------------------------------------------
+| time/                   |              |
+|    fps                  | 845          |
+|    iterations           | 2            |
+|    time_elapsed         | 4            |
+|    total_timesteps      | 4096         |
+| train/                  |              |
+|    approx_kl            | 0.0006865192 |
+|    clip_fraction        | 0.00366      |
+|    clip_range           | 0.15         |
+|    entropy_loss         | -8.32        |
+|    explained_variance   | -3.63        |
+|    learning_rate        | 0.00025      |
+|    loss                 | -0.00368     |
+|    n_updates            | 4            |
+|    policy_gradient_loss | -0.00183     |
+|    value_loss           | 0.558        |
+------------------------------------------
+-----------------------------------------
+| time/                   |             |
+|    fps                  | 946         |
+|    iterations           | 3           |
+|    time_elapsed         | 6           |
+|    total_timesteps      | 6144        |
+| train/                  |             |
+|    approx_kl            | 0.002127223 |
+|    clip_fraction        | 0.00745     |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.32       |
+|    explained_variance   | -1.02       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0243     |
+|    n_updates            | 8           |
+|    policy_gradient_loss | -0.00382    |
+|    value_loss           | 0.000556    |
+-----------------------------------------
+------------------------------------------
+| time/                   |              |
+|    fps                  | 1010         |
+|    iterations           | 4            |
+|    time_elapsed         | 8            |
+|    total_timesteps      | 8192         |
+| train/                  |              |
+|    approx_kl            | 0.0044984696 |
+|    clip_fraction        | 0.0646       |
+|    clip_range           | 0.15         |
+|    entropy_loss         | -8.3         |
+|    explained_variance   | -0.638       |
+|    learning_rate        | 0.00025      |
+|    loss                 | -0.0175      |
+|    n_updates            | 12           |
+|    policy_gradient_loss | -0.0108      |
+|    value_loss           | 0.000658     |
+------------------------------------------
+-----------------------------------------
+| time/                   |             |
+|    fps                  | 1061        |
+|    iterations           | 5           |
+|    time_elapsed         | 9           |
+|    total_timesteps      | 10240       |
+| train/                  |             |
+|    approx_kl            | 0.005734851 |
+|    clip_fraction        | 0.0897      |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.3        |
+|    explained_variance   | -1.8        |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0464     |
+|    n_updates            | 16          |
+|    policy_gradient_loss | -0.0206     |
+|    value_loss           | 0.000854    |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 641         |
+|    ep_rew_mean          | -1.03       |
+| time/                   |             |
+|    fps                  | 1094        |
+|    iterations           | 6           |
+|    time_elapsed         | 11          |
+|    total_timesteps      | 12288       |
+| train/                  |             |
+|    approx_kl            | 0.009480905 |
+|    clip_fraction        | 0.168       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.3        |
+|    explained_variance   | -1.48       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0465     |
+|    n_updates            | 20          |
+|    policy_gradient_loss | -0.0279     |
+|    value_loss           | 0.00133     |
+-----------------------------------------
+------------------------------------------
+| rollout/                |              |
+|    ep_len_mean          | 776          |
+|    ep_rew_mean          | -0.902       |
+| time/                   |              |
+|    fps                  | 1123         |
+|    iterations           | 7            |
+|    time_elapsed         | 12           |
+|    total_timesteps      | 14336        |
+| train/                  |              |
+|    approx_kl            | 0.0110367015 |
+|    clip_fraction        | 0.218        |
+|    clip_range           | 0.15         |
+|    entropy_loss         | -8.29        |
+|    explained_variance   | -2.01        |
+|    learning_rate        | 0.00025      |
+|    loss                 | -0.0616      |
+|    n_updates            | 24           |
+|    policy_gradient_loss | -0.0323      |
+|    value_loss           | 0.00166      |
+------------------------------------------
+------------------------------------------
+| rollout/                |              |
+|    ep_len_mean          | 859          |
+|    ep_rew_mean          | -0.697       |
+| time/                   |              |
+|    fps                  | 1142         |
+|    iterations           | 8            |
+|    time_elapsed         | 14           |
+|    total_timesteps      | 16384        |
+| train/                  |              |
+|    approx_kl            | 0.0151232965 |
+|    clip_fraction        | 0.223        |
+|    clip_range           | 0.15         |
+|    entropy_loss         | -8.28        |
+|    explained_variance   | -0.845       |
+|    learning_rate        | 0.00025      |
+|    loss                 | -0.0603      |
+|    n_updates            | 28           |
+|    policy_gradient_loss | -0.0354      |
+|    value_loss           | 0.00258      |
+------------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 906         |
+|    ep_rew_mean          | -0.58       |
+| time/                   |             |
+|    fps                  | 1158        |
+|    iterations           | 9           |
+|    time_elapsed         | 15          |
+|    total_timesteps      | 18432       |
+| train/                  |             |
+|    approx_kl            | 0.014987826 |
+|    clip_fraction        | 0.219       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.27       |
+|    explained_variance   | -3.09       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0666     |
+|    n_updates            | 32          |
+|    policy_gradient_loss | -0.0375     |
+|    value_loss           | 0.00238     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 906         |
+|    ep_rew_mean          | -0.58       |
+| time/                   |             |
+|    fps                  | 1173        |
+|    iterations           | 10          |
+|    time_elapsed         | 17          |
+|    total_timesteps      | 20480       |
+| train/                  |             |
+|    approx_kl            | 0.013858849 |
+|    clip_fraction        | 0.24        |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.28       |
+|    explained_variance   | -3.52       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0808     |
+|    n_updates            | 36          |
+|    policy_gradient_loss | -0.0403     |
+|    value_loss           | 0.00161     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 905         |
+|    ep_rew_mean          | -0.592      |
+| time/                   |             |
+|    fps                  | 1186        |
+|    iterations           | 11          |
+|    time_elapsed         | 18          |
+|    total_timesteps      | 22528       |
+| train/                  |             |
+|    approx_kl            | 0.015846182 |
+|    clip_fraction        | 0.269       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.26       |
+|    explained_variance   | -2.35       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.079      |
+|    n_updates            | 40          |
+|    policy_gradient_loss | -0.0387     |
+|    value_loss           | 0.00168     |
+-----------------------------------------
+----------------------------------------
+| rollout/                |            |
+|    ep_len_mean          | 984        |
+|    ep_rew_mean          | -0.53      |
+| time/                   |            |
+|    fps                  | 1195       |
+|    iterations           | 12         |
+|    time_elapsed         | 20         |
+|    total_timesteps      | 24576      |
+| train/                  |            |
+|    approx_kl            | 0.01491547 |
+|    clip_fraction        | 0.255      |
+|    clip_range           | 0.15       |
+|    entropy_loss         | -8.25      |
+|    explained_variance   | -1.11      |
+|    learning_rate        | 0.00025    |
+|    loss                 | -0.062     |
+|    n_updates            | 44         |
+|    policy_gradient_loss | -0.0362    |
+|    value_loss           | 0.00198    |
+----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.03e+03    |
+|    ep_rew_mean          | -0.512      |
+| time/                   |             |
+|    fps                  | 1201        |
+|    iterations           | 13          |
+|    time_elapsed         | 22          |
+|    total_timesteps      | 26624       |
+| train/                  |             |
+|    approx_kl            | 0.016814027 |
+|    clip_fraction        | 0.26        |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.25       |
+|    explained_variance   | -2.27       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0743     |
+|    n_updates            | 48          |
+|    policy_gradient_loss | -0.0419     |
+|    value_loss           | 0.00208     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.08e+03    |
+|    ep_rew_mean          | -0.479      |
+| time/                   |             |
+|    fps                  | 1207        |
+|    iterations           | 14          |
+|    time_elapsed         | 23          |
+|    total_timesteps      | 28672       |
+| train/                  |             |
+|    approx_kl            | 0.016041683 |
+|    clip_fraction        | 0.288       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.23       |
+|    explained_variance   | -3.28       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0766     |
+|    n_updates            | 52          |
+|    policy_gradient_loss | -0.0389     |
+|    value_loss           | 0.00205     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.07e+03    |
+|    ep_rew_mean          | -0.466      |
+| time/                   |             |
+|    fps                  | 1210        |
+|    iterations           | 15          |
+|    time_elapsed         | 25          |
+|    total_timesteps      | 30720       |
+| train/                  |             |
+|    approx_kl            | 0.015445614 |
+|    clip_fraction        | 0.297       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.2        |
+|    explained_variance   | -2.72       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0713     |
+|    n_updates            | 56          |
+|    policy_gradient_loss | -0.038      |
+|    value_loss           | 0.0017      |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.11e+03    |
+|    ep_rew_mean          | -0.439      |
+| time/                   |             |
+|    fps                  | 1216        |
+|    iterations           | 16          |
+|    time_elapsed         | 26          |
+|    total_timesteps      | 32768       |
+| train/                  |             |
+|    approx_kl            | 0.015225915 |
+|    clip_fraction        | 0.281       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.2        |
+|    explained_variance   | -3.47       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0782     |
+|    n_updates            | 60          |
+|    policy_gradient_loss | -0.039      |
+|    value_loss           | 0.00181     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.11e+03    |
+|    ep_rew_mean          | -0.392      |
+| time/                   |             |
+|    fps                  | 1221        |
+|    iterations           | 17          |
+|    time_elapsed         | 28          |
+|    total_timesteps      | 34816       |
+| train/                  |             |
+|    approx_kl            | 0.015854025 |
+|    clip_fraction        | 0.271       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.2        |
+|    explained_variance   | -2.11       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0787     |
+|    n_updates            | 64          |
+|    policy_gradient_loss | -0.0383     |
+|    value_loss           | 0.00166     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.13e+03    |
+|    ep_rew_mean          | -0.393      |
+| time/                   |             |
+|    fps                  | 1219        |
+|    iterations           | 18          |
+|    time_elapsed         | 30          |
+|    total_timesteps      | 36864       |
+| train/                  |             |
+|    approx_kl            | 0.019244976 |
+|    clip_fraction        | 0.281       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.23       |
+|    explained_variance   | -2.54       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0813     |
+|    n_updates            | 68          |
+|    policy_gradient_loss | -0.0459     |
+|    value_loss           | 0.00148     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.16e+03    |
+|    ep_rew_mean          | -0.374      |
+| time/                   |             |
+|    fps                  | 1221        |
+|    iterations           | 19          |
+|    time_elapsed         | 31          |
+|    total_timesteps      | 38912       |
+| train/                  |             |
+|    approx_kl            | 0.019559633 |
+|    clip_fraction        | 0.282       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.23       |
+|    explained_variance   | -2.18       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0707     |
+|    n_updates            | 72          |
+|    policy_gradient_loss | -0.0458     |
+|    value_loss           | 0.00162     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.15e+03    |
+|    ep_rew_mean          | -0.384      |
+| time/                   |             |
+|    fps                  | 1225        |
+|    iterations           | 20          |
+|    time_elapsed         | 33          |
+|    total_timesteps      | 40960       |
+| train/                  |             |
+|    approx_kl            | 0.018351572 |
+|    clip_fraction        | 0.297       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.23       |
+|    explained_variance   | -3.07       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0792     |
+|    n_updates            | 76          |
+|    policy_gradient_loss | -0.0437     |
+|    value_loss           | 0.00149     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.18e+03    |
+|    ep_rew_mean          | -0.372      |
+| time/                   |             |
+|    fps                  | 1228        |
+|    iterations           | 21          |
+|    time_elapsed         | 35          |
+|    total_timesteps      | 43008       |
+| train/                  |             |
+|    approx_kl            | 0.020750236 |
+|    clip_fraction        | 0.297       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.21       |
+|    explained_variance   | -3.2        |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0878     |
+|    n_updates            | 80          |
+|    policy_gradient_loss | -0.0498     |
+|    value_loss           | 0.00151     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.18e+03    |
+|    ep_rew_mean          | -0.372      |
+| time/                   |             |
+|    fps                  | 1230        |
+|    iterations           | 22          |
+|    time_elapsed         | 36          |
+|    total_timesteps      | 45056       |
+| train/                  |             |
+|    approx_kl            | 0.021276187 |
+|    clip_fraction        | 0.309       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.2        |
+|    explained_variance   | -1.92       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0831     |
+|    n_updates            | 84          |
+|    policy_gradient_loss | -0.0436     |
+|    value_loss           | 0.00154     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.18e+03    |
+|    ep_rew_mean          | -0.372      |
+| time/                   |             |
+|    fps                  | 1235        |
+|    iterations           | 23          |
+|    time_elapsed         | 38          |
+|    total_timesteps      | 47104       |
+| train/                  |             |
+|    approx_kl            | 0.025053155 |
+|    clip_fraction        | 0.331       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.19       |
+|    explained_variance   | -2.63       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0852     |
+|    n_updates            | 88          |
+|    policy_gradient_loss | -0.0462     |
+|    value_loss           | 0.00145     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.19e+03    |
+|    ep_rew_mean          | -0.359      |
+| time/                   |             |
+|    fps                  | 1238        |
+|    iterations           | 24          |
+|    time_elapsed         | 39          |
+|    total_timesteps      | 49152       |
+| train/                  |             |
+|    approx_kl            | 0.020340582 |
+|    clip_fraction        | 0.307       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.19       |
+|    explained_variance   | -1.63       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0892     |
+|    n_updates            | 92          |
+|    policy_gradient_loss | -0.0441     |
+|    value_loss           | 0.00132     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.2e+03     |
+|    ep_rew_mean          | -0.348      |
+| time/                   |             |
+|    fps                  | 1239        |
+|    iterations           | 25          |
+|    time_elapsed         | 41          |
+|    total_timesteps      | 51200       |
+| train/                  |             |
+|    approx_kl            | 0.021488946 |
+|    clip_fraction        | 0.356       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.2        |
+|    explained_variance   | -2.75       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0726     |
+|    n_updates            | 96          |
+|    policy_gradient_loss | -0.0409     |
+|    value_loss           | 0.0015      |
+-----------------------------------------
+----------------------------------------
+| rollout/                |            |
+|    ep_len_mean          | 1.25e+03   |
+|    ep_rew_mean          | -0.305     |
+| time/                   |            |
+|    fps                  | 1239       |
+|    iterations           | 26         |
+|    time_elapsed         | 42         |
+|    total_timesteps      | 53248      |
+| train/                  |            |
+|    approx_kl            | 0.02217162 |
+|    clip_fraction        | 0.343      |
+|    clip_range           | 0.15       |
+|    entropy_loss         | -8.21      |
+|    explained_variance   | -4.22      |
+|    learning_rate        | 0.00025    |
+|    loss                 | -0.0876    |
+|    n_updates            | 100        |
+|    policy_gradient_loss | -0.0484    |
+|    value_loss           | 0.00121    |
+----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.28e+03    |
+|    ep_rew_mean          | -0.291      |
+| time/                   |             |
+|    fps                  | 1241        |
+|    iterations           | 27          |
+|    time_elapsed         | 44          |
+|    total_timesteps      | 55296       |
+| train/                  |             |
+|    approx_kl            | 0.021241352 |
+|    clip_fraction        | 0.326       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.22       |
+|    explained_variance   | -2.21       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0815     |
+|    n_updates            | 104         |
+|    policy_gradient_loss | -0.0436     |
+|    value_loss           | 0.0011      |
+-----------------------------------------
+----------------------------------------
+| rollout/                |            |
+|    ep_len_mean          | 1.3e+03    |
+|    ep_rew_mean          | -0.267     |
+| time/                   |            |
+|    fps                  | 1242       |
+|    iterations           | 28         |
+|    time_elapsed         | 46         |
+|    total_timesteps      | 57344      |
+| train/                  |            |
+|    approx_kl            | 0.02174874 |
+|    clip_fraction        | 0.315      |
+|    clip_range           | 0.15       |
+|    entropy_loss         | -8.2       |
+|    explained_variance   | -3.67      |
+|    learning_rate        | 0.00025    |
+|    loss                 | -0.0848    |
+|    n_updates            | 108        |
+|    policy_gradient_loss | -0.0471    |
+|    value_loss           | 0.00107    |
+----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.3e+03     |
+|    ep_rew_mean          | -0.267      |
+| time/                   |             |
+|    fps                  | 1243        |
+|    iterations           | 29          |
+|    time_elapsed         | 47          |
+|    total_timesteps      | 59392       |
+| train/                  |             |
+|    approx_kl            | 0.021925835 |
+|    clip_fraction        | 0.335       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.19       |
+|    explained_variance   | -1.94       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0844     |
+|    n_updates            | 112         |
+|    policy_gradient_loss | -0.0451     |
+|    value_loss           | 0.00102     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.3e+03     |
+|    ep_rew_mean          | -0.259      |
+| time/                   |             |
+|    fps                  | 1243        |
+|    iterations           | 30          |
+|    time_elapsed         | 49          |
+|    total_timesteps      | 61440       |
+| train/                  |             |
+|    approx_kl            | 0.024559658 |
+|    clip_fraction        | 0.323       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.2        |
+|    explained_variance   | -2.53       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0829     |
+|    n_updates            | 116         |
+|    policy_gradient_loss | -0.0504     |
+|    value_loss           | 0.00119     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.31e+03    |
+|    ep_rew_mean          | -0.242      |
+| time/                   |             |
+|    fps                  | 1244        |
+|    iterations           | 31          |
+|    time_elapsed         | 51          |
+|    total_timesteps      | 63488       |
+| train/                  |             |
+|    approx_kl            | 0.028697252 |
+|    clip_fraction        | 0.348       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.19       |
+|    explained_variance   | -3.28       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0895     |
+|    n_updates            | 120         |
+|    policy_gradient_loss | -0.0496     |
+|    value_loss           | 0.0012      |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.3e+03     |
+|    ep_rew_mean          | -0.246      |
+| time/                   |             |
+|    fps                  | 1244        |
+|    iterations           | 32          |
+|    time_elapsed         | 52          |
+|    total_timesteps      | 65536       |
+| train/                  |             |
+|    approx_kl            | 0.022997446 |
+|    clip_fraction        | 0.334       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.2        |
+|    explained_variance   | -2.49       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0787     |
+|    n_updates            | 124         |
+|    policy_gradient_loss | -0.0452     |
+|    value_loss           | 0.00111     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.3e+03     |
+|    ep_rew_mean          | -0.246      |
+| time/                   |             |
+|    fps                  | 1244        |
+|    iterations           | 33          |
+|    time_elapsed         | 54          |
+|    total_timesteps      | 67584       |
+| train/                  |             |
+|    approx_kl            | 0.024152309 |
+|    clip_fraction        | 0.34        |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.21       |
+|    explained_variance   | -1.53       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0803     |
+|    n_updates            | 128         |
+|    policy_gradient_loss | -0.0436     |
+|    value_loss           | 0.00116     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.3e+03     |
+|    ep_rew_mean          | -0.243      |
+| time/                   |             |
+|    fps                  | 1244        |
+|    iterations           | 34          |
+|    time_elapsed         | 55          |
+|    total_timesteps      | 69632       |
+| train/                  |             |
+|    approx_kl            | 0.022691075 |
+|    clip_fraction        | 0.325       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.18       |
+|    explained_variance   | -1.67       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0822     |
+|    n_updates            | 132         |
+|    policy_gradient_loss | -0.0478     |
+|    value_loss           | 0.00113     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.29e+03    |
+|    ep_rew_mean          | -0.239      |
+| time/                   |             |
+|    fps                  | 1247        |
+|    iterations           | 35          |
+|    time_elapsed         | 57          |
+|    total_timesteps      | 71680       |
+| train/                  |             |
+|    approx_kl            | 0.023983043 |
+|    clip_fraction        | 0.343       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.18       |
+|    explained_variance   | -2.29       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0878     |
+|    n_updates            | 136         |
+|    policy_gradient_loss | -0.0478     |
+|    value_loss           | 0.00114     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.29e+03    |
+|    ep_rew_mean          | -0.226      |
+| time/                   |             |
+|    fps                  | 1250        |
+|    iterations           | 36          |
+|    time_elapsed         | 58          |
+|    total_timesteps      | 73728       |
+| train/                  |             |
+|    approx_kl            | 0.023928368 |
+|    clip_fraction        | 0.339       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.16       |
+|    explained_variance   | -3.01       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0875     |
+|    n_updates            | 140         |
+|    policy_gradient_loss | -0.0508     |
+|    value_loss           | 0.00123     |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.29e+03    |
+|    ep_rew_mean          | -0.226      |
+| time/                   |             |
+|    fps                  | 1251        |
+|    iterations           | 37          |
+|    time_elapsed         | 60          |
+|    total_timesteps      | 75776       |
+| train/                  |             |
+|    approx_kl            | 0.029227499 |
+|    clip_fraction        | 0.362       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.16       |
+|    explained_variance   | -2.77       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0825     |
+|    n_updates            | 144         |
+|    policy_gradient_loss | -0.0478     |
+|    value_loss           | 0.00105     |
+-----------------------------------------
+----------------------------------------
+| rollout/                |            |
+|    ep_len_mean          | 1.29e+03   |
+|    ep_rew_mean          | -0.226     |
+| time/                   |            |
+|    fps                  | 1254       |
+|    iterations           | 38         |
+|    time_elapsed         | 62         |
+|    total_timesteps      | 77824      |
+| train/                  |            |
+|    approx_kl            | 0.02629639 |
+|    clip_fraction        | 0.377      |
+|    clip_range           | 0.15       |
+|    entropy_loss         | -8.17      |
+|    explained_variance   | -1.49      |
+|    learning_rate        | 0.00025    |
+|    loss                 | -0.0794    |
+|    n_updates            | 148        |
+|    policy_gradient_loss | -0.0471    |
+|    value_loss           | 0.00104    |
+----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.3e+03     |
+|    ep_rew_mean          | -0.221      |
+| time/                   |             |
+|    fps                  | 1255        |
+|    iterations           | 39          |
+|    time_elapsed         | 63          |
+|    total_timesteps      | 79872       |
+| train/                  |             |
+|    approx_kl            | 0.024264112 |
+|    clip_fraction        | 0.348       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.16       |
+|    explained_variance   | -1.81       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0864     |
+|    n_updates            | 152         |
+|    policy_gradient_loss | -0.0496     |
+|    value_loss           | 0.00101     |
+-----------------------------------------
+----------------------------------------
+| rollout/                |            |
+|    ep_len_mean          | 1.31e+03   |
+|    ep_rew_mean          | -0.217     |
+| time/                   |            |
+|    fps                  | 1257       |
+|    iterations           | 40         |
+|    time_elapsed         | 65         |
+|    total_timesteps      | 81920      |
+| train/                  |            |
+|    approx_kl            | 0.03137113 |
+|    clip_fraction        | 0.385      |
+|    clip_range           | 0.15       |
+|    entropy_loss         | -8.14      |
+|    explained_variance   | -2.58      |
+|    learning_rate        | 0.00025    |
+|    loss                 | -0.0907    |
+|    n_updates            | 156        |
+|    policy_gradient_loss | -0.0514    |
+|    value_loss           | 0.000992   |
+----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.31e+03    |
+|    ep_rew_mean          | -0.217      |
+| time/                   |             |
+|    fps                  | 1259        |
+|    iterations           | 41          |
+|    time_elapsed         | 66          |
+|    total_timesteps      | 83968       |
+| train/                  |             |
+|    approx_kl            | 0.026189182 |
+|    clip_fraction        | 0.37        |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.13       |
+|    explained_variance   | -1.83       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0842     |
+|    n_updates            | 160         |
+|    policy_gradient_loss | -0.0472     |
+|    value_loss           | 0.00098     |
+-----------------------------------------
+---------------------------------------
+| rollout/                |           |
+|    ep_len_mean          | 1.32e+03  |
+|    ep_rew_mean          | -0.21     |
+| time/                   |           |
+|    fps                  | 1261      |
+|    iterations           | 42        |
+|    time_elapsed         | 68        |
+|    total_timesteps      | 86016     |
+| train/                  |           |
+|    approx_kl            | 0.0249079 |
+|    clip_fraction        | 0.335     |
+|    clip_range           | 0.15      |
+|    entropy_loss         | -8.14     |
+|    explained_variance   | -1.64     |
+|    learning_rate        | 0.00025   |
+|    loss                 | -0.0877   |
+|    n_updates            | 164       |
+|    policy_gradient_loss | -0.0472   |
+|    value_loss           | 0.000896  |
+---------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.32e+03    |
+|    ep_rew_mean          | -0.21       |
+| time/                   |             |
+|    fps                  | 1263        |
+|    iterations           | 43          |
+|    time_elapsed         | 69          |
+|    total_timesteps      | 88064       |
+| train/                  |             |
+|    approx_kl            | 0.025553392 |
+|    clip_fraction        | 0.346       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.14       |
+|    explained_variance   | -1.44       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0829     |
+|    n_updates            | 168         |
+|    policy_gradient_loss | -0.0451     |
+|    value_loss           | 0.000811    |
+-----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.34e+03    |
+|    ep_rew_mean          | -0.198      |
+| time/                   |             |
+|    fps                  | 1265        |
+|    iterations           | 44          |
+|    time_elapsed         | 71          |
+|    total_timesteps      | 90112       |
+| train/                  |             |
+|    approx_kl            | 0.027851868 |
+|    clip_fraction        | 0.336       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.11       |
+|    explained_variance   | -3.01       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0836     |
+|    n_updates            | 172         |
+|    policy_gradient_loss | -0.0495     |
+|    value_loss           | 0.000813    |
+-----------------------------------------
+----------------------------------------
+| rollout/                |            |
+|    ep_len_mean          | 1.34e+03   |
+|    ep_rew_mean          | -0.2       |
+| time/                   |            |
+|    fps                  | 1266       |
+|    iterations           | 45         |
+|    time_elapsed         | 72         |
+|    total_timesteps      | 92160      |
+| train/                  |            |
+|    approx_kl            | 0.03102021 |
+|    clip_fraction        | 0.343      |
+|    clip_range           | 0.15       |
+|    entropy_loss         | -8.11      |
+|    explained_variance   | -2.36      |
+|    learning_rate        | 0.00025    |
+|    loss                 | -0.0771    |
+|    n_updates            | 176        |
+|    policy_gradient_loss | -0.0518    |
+|    value_loss           | 0.000777   |
+----------------------------------------
+-----------------------------------------
+| rollout/                |             |
+|    ep_len_mean          | 1.34e+03    |
+|    ep_rew_mean          | -0.206      |
+| time/                   |             |
+|    fps                  | 1266        |
+|    iterations           | 46          |
+|    time_elapsed         | 74          |
+|    total_timesteps      | 94208       |
+| train/                  |             |
+|    approx_kl            | 0.032389328 |
+|    clip_fraction        | 0.395       |
+|    clip_range           | 0.15        |
+|    entropy_loss         | -8.11       |
+|    explained_variance   | -2.11       |
+|    learning_rate        | 0.00025     |
+|    loss                 | -0.0933     |
+|    n_updates            | 180         |
+|    policy_gradient_loss | -0.0508     |
+|    value_loss           | 0.00094     |
+-----------------------------------------

006_rgb_stack_ram_based_reward_time_reward_final_round/tune_ppo.py

@@ -0,0 +1,68 @@
+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)

data.json

@@ -0,0 +1,60 @@
+{
+    "info": {
+        "enemy_character": {
+            "address": 16745563,
+            "type": "|u1"
+        },
+        "agent_hp": {
+            "address": 16744514,
+            "type": ">i2"
+        },
+        "agent_x": {
+            "address": 16744454,
+            "type": ">u2"
+        },
+        "agent_y": {
+            "address": 16744458,
+            "type": ">u2"
+        },
+        "enemy_hp": {
+            "address": 16745154,
+            "type": ">i2"
+        },
+        "enemy_x": {
+            "address": 16745094,
+            "type": ">u2"
+        },
+        "enemy_y": {
+            "address": 16745098,
+            "type": ">u2"
+        },
+        "score": {
+            "address": 16744936,
+            "type": ">d4"
+        },
+        "agent_victories": {
+            "address": 16744922,
+            "type": "|u1"
+        },
+        "enemy_victories": {
+            "address": 16745559,
+            "type": ">u4"
+        },
+        "round_countdown": {
+            "address": 16750378,
+            "type": ">u2"
+        },
+        "reset_countdown": {
+            "address": 16744917,
+            "type": "|u1"
+        },
+        "agent_status": {
+            "address": 16744450,
+            "type": ">u2"
+        },
+        "enemy_status": {
+            "address": 16745090,
+            "type": ">u2"
+        }
+    }
+}