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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
}
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