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JaiSurya/
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

This model is trained with the help of Deep RL Course by HuggingFace

Usage (with Stable-baselines3)

python

# necessary libraries
import gymnasium as gym

from huggingface_sb3 import load_from_hub, package_to_hub
from huggingface_hub import (
    notebook_login,
)

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

# Step 1 : Create an environment
env = gym.make("LunarLander-v2")
observation,info = env.reset() # initialize the environment

# Step 2 : Create the model
model = PPO(
    policy = "MlpPolicy", # Multiple Layer Perceptron Policy
    env = env,
    n_steps = 1024,
    batch_size = 64,
    n_epochs = 5,
    gamma = 0.995,
    gae_lambda = 0.98,
    ent_coef = 0.0001,
    clip_range = 0.1,
    verbose = 1
)

# Step 3 : Train the model
model.learn(total_timesteps=2500000,progress_bar = True)

# Step 4 : Evaluation
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} +/- {std_reward}")

JaiSurya/PPO-LunarLander-v2

PPO Agent playing LunarLander-v2

Usage (with Stable-baselines3)

JaiSurya/
PPO-LunarLander-v2