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---
tags:
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
- atari
model-index:
- name: PPO Agent
results:
- task:
type: reinforcement-learning # Required. Example: automatic-speech-recognition
dataset:
type: SeaquestNoFrameskip-v4 # Required. Example: common_voice. Use dataset id from https://hf.co/datasets
name: SeaquestNoFrameskip-v4 # Required. Example: Common Voice zh-CN
metrics:
- type: mean_reward # Required. Example: wer
value: 1820.00 +/- 20.0 # Required. Example: 20.90
---
# PPO Agent playing SeaquestNoFrameskip-v4
This is a trained model of a **PPO agent playing SeaquestNoFrameskip-v4 using the [stable-baselines3 library](https://stable-baselines3.readthedocs.io/en/master/index.html)**.
The training report: https://wandb.ai/simoninithomas/HFxSB3/reports/Atari-HFxSB3-Benchmark--VmlldzoxNjI3NTIy
## Evaluation Results
Mean_reward: `1820.00 +/- 20.0`
# Usage (with Stable-baselines3)
- You need to use `gym==0.19` since it **includes Atari Roms**.
- The Action Space is 6 since we use only **possible actions in this game**.
Watch your agent interacts :
```python
# Import the libraries
import os
import gym
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import VecNormalize
from stable_baselines3.common.env_util import make_atari_env
from stable_baselines3.common.vec_env import VecFrameStack
from huggingface_sb3 import load_from_hub, push_to_hub
# Load the model
checkpoint = load_from_hub("ThomasSimonini/ppo-SeaquestNoFrameskip-v4", "ppo-SeaquestNoFrameskip-v4.zip")
# Because we using 3.7 on Colab and this agent was trained with 3.8 to avoid Pickle errors:
custom_objects = {
"learning_rate": 0.0,
"lr_schedule": lambda _: 0.0,
"clip_range": lambda _: 0.0,
}
model= PPO.load(checkpoint, custom_objects=custom_objects)
env = make_atari_env('SeaquestNoFrameskip-v4', n_envs=1)
env = VecFrameStack(env, n_stack=4)
obs = env.reset()
while True:
action, _states = model.predict(obs)
obs, rewards, dones, info = env.step(action)
env.render()
```
## Training Code
```python
import wandb
import gym
from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_atari_env
from stable_baselines3.common.vec_env import VecFrameStack, VecVideoRecorder
from stable_baselines3.common.callbacks import CheckpointCallback
from wandb.integration.sb3 import WandbCallback
from huggingface_sb3 import load_from_hub, push_to_hub
config = {
"env_name": "SeaquestNoFrameskip-v4",
"num_envs": 8,
"total_timesteps": int(10e6),
"seed": 2862830927,
}
run = wandb.init(
project="HFxSB3",
config = config,
sync_tensorboard = True, # Auto-upload sb3's tensorboard metrics
monitor_gym = True, # Auto-upload the videos of agents playing the game
save_code = True, # Save the code to W&B
)
# There already exists an environment generator
# that will make and wrap atari environments correctly.
# Here we are also multi-worker training (n_envs=8 => 8 environments)
env = make_atari_env(config["env_name"], n_envs=config["num_envs"], seed=config["seed"]) #SeaquestNoFrameskip-v4
print("ENV ACTION SPACE: ", env.action_space.n)
# Frame-stacking with 4 frames
env = VecFrameStack(env, n_stack=4)
# Video recorder
env = VecVideoRecorder(env, "videos", record_video_trigger=lambda x: x % 100000 == 0, video_length=2000)
model = PPO(policy = "CnnPolicy",
env = env,
batch_size = 256,
clip_range = 0.1,
ent_coef = 0.01,
gae_lambda = 0.9,
gamma = 0.99,
learning_rate = 2.5e-4,
max_grad_norm = 0.5,
n_epochs = 4,
n_steps = 128,
vf_coef = 0.5,
tensorboard_log = f"runs",
verbose=1,
)
model.learn(
total_timesteps = config["total_timesteps"],
callback = [
WandbCallback(
gradient_save_freq = 1000,
model_save_path = f"models/{run.id}",
),
CheckpointCallback(save_freq=10000, save_path='./seaquest',
name_prefix=config["env_name"]),
]
)
model.save("ppo-SeaquestNoFrameskip-v4.zip")
push_to_hub(repo_id="ThomasSimonini/ppo-SeaquestNoFrameskip-v4",
filename="ppo-SeaquestNoFrameskip-v4.zip",
commit_message="Added Seaquest trained agent")
```
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