PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2. The agent has been trained with a custom PPO implementation inspired to a tutorial by Costa Huang.
This work is related to Unit 8, part 1 of the Hugging Face Deep RL course. I had to slightly modify some pieces of the provided notebook, because I used gymnasium and not gym. Furthermore, the PPO implementation is available on GitHub, here: https://github.com/micdestefano/micppo.
Hyperparameters
{
'exp_name': 'micppo'
'gym_id': 'LunarLander-v2'
'learning_rate': 0.00025
'min_learning_rate_ratio': 0.01
'seed': 1
'total_timesteps': 10000000
'torch_not_deterministic': False
'no_cuda': False
'capture_video': True
'hidden_size': 256
'num_hidden_layers': 3
'activation': 'leaky-relu'
'num_checkpoints': 4
'num_envs': 8
'num_steps': 2048
'no_lr_annealing': False
'no_gae': False
'gamma': 0.99
'gae_lambda': 0.95
'num_minibatches': 16
'num_update_epochs': 32
'no_advantage_normalization': False
'clip_coef': 0.2
'no_value_loss_clip': False
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'batch_size': 16384
'minibatch_size': 1024
}
Evaluation results
- mean_reward on LunarLander-v2self-reported301.97 +/- 19.65