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Allejandro/
ppo-LunarLander-v2

Name: ppo-LunarLander-v2
Availability: InStock

stable-baselines3

LunarLander-v2

deep-reinforcement-learning

reinforcement-learning

model-index

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PPO Agent playing LunarLander-v2

This is a trained model of a PPO agent playing LunarLander-v2 using the stable-baselines3 library.

Usage (with Stable-baselines3)

python

import gymnasium

from stable_baselines3 import PPO
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.common.monitor import Monitor
from huggingface_sb3 import load_from_hub


# Create the environment
env = make_vec_env('LunarLander-v2', n_envs=16)

# Define a PPO MlpPolicy architecture
# We use MultiLayerPerceptron (MLPPolicy) because the input is a vector,
# if we had frames as input we would use CnnPolicy
model = PPO(
    "MlpPolicy",
    env = env,
    n_steps = 1024,
    batch_size = 64,
    n_epochs = 4,
    gamma = 0.999,
    gae_lambda = 0.98,
    ent_coef = 0.01,
    verbose=1)

# Train it for 1,000,000 timesteps
model.learn(total_timesteps=1000000)
# Specify file name for model and save the model to file
model_name = "ppo-LunarLander-v2"
model.save(model_name)

# Evaluate the agent
# Create a new environment for evaluation
eval_env = Monitor(gym.make("LunarLander-v2"))

# Evaluate the model with 10 evaluation episodes and deterministic=True
mean_reward, std_reward = evaluate_policy(model=model, env=eval_env, n_eval_episodes=10, deterministic=True) 

# Print the results
print(mean_reward)
print(std_reward)
...

Allejandro/ppo-LunarLander-v2

PPO Agent playing LunarLander-v2

Usage (with Stable-baselines3)

Allejandro/
ppo-LunarLander-v2