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Casio991ms/
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)

TODO: Add your code

python

import gymnasium as gym

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

env = make_vec_env('LunarLander-v2', n_envs=16)

model = PPO(
    policy='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)

model.learn(total_timesteps=1000000)
model_name = "ppo-LunarLander-v2"
model.save(model_name)


eval_env = Monitor(gym.make("LunarLander-v2"))
mean_reward, std_reward = evaluate_policy(model, eval_env, n_eval_episodes=10, deterministic=True)
print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

Casio991ms/PPO-LunarLander-v2

PPO Agent playing LunarLander-v2

Usage (with Stable-baselines3)

Casio991ms/
PPO-LunarLander-v2