metadata
tags:
- LunarLander-v2
- ppo
- deep-reinforcement-learning
- reinforcement-learning
- custom-implementation
- deep-rl-course
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 292.50 +/- 11.20
name: mean_reward
verified: false
PPO Agent Playing LunarLander-v2
This is a trained model of a PPO (Proximal Policy Optimization) agent playing LunarLander-v2 implemented from scratch with PyTorch using the CleanRL framework.
🏆 Evaluation Results
- Mean Reward: 292.50 +/- 11.20
- Lower Bound Result (Mean - Std): 281.30
- Episodes Evaluated: 10
- Status: Solved (Environment threshold: 200.0)
Hyperparameters
{
'anneal_lr': True,
'batch_size': 2048,
'capture_video': False,
'clip_coef': 0.2,
'clip_vloss': True,
'cuda': True,
'ent_coef': 0.01,
'env_id': 'LunarLander-v2',
'exp_name': 'ppo',
'gae': True,
'gae_lambda': 0.95,
'gamma': 0.99,
'learning_rate': 0.00025,
'max_grad_norm': 0.5,
'num_envs': 16,
'num_minibatches': 4,
'num_steps': 128,
'repo_id': 'umesh251/ppo-LunarLander-v2',
'seed': 1,
'total_timesteps': 500000,
'update_epochs': 4,
'vf_coef': 0.5
}
How to use
import gym
import torch
from test_agent import Agent
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
env = gym.make("LunarLander-v2")
agent = Agent(env).to(device)