riteesh11/ppo-LunarLander-v2-custom

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.

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

python { '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': 'riteesh11/ppo-LunarLander-v2-custom', 'seed': 1, 'total_timesteps': 500000, 'update_epochs': 4, 'vf_coef': 0.5 }

How to use

`python 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) `