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---
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: 301.97 +/- 19.65
name: mean_reward
verified: false
---
# 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](https://www.youtube.com/watch?v=MEt6rrxH8W4).
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](https://github.com/micdestefano/micppo).
# Hyperparameters
```python
{
'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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