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ppo-lunarlander
tanu20/ppo-lunarlander
ppo-lunarlander is a reinforcement learning model from tanu20. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product.
This repository contains a PPO (Proximal Policy Optimization) agent implemented in PyTorch and trained on the LunarLander environment as part of the Hugging Face Deep Reinforcement Learning Course — Unit 8 Part I.
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Public
Updated Sep 23, 2026
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321 KB
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.mp4234 KB · 72%
At a glance
- Task
- Reinforcement Learning
- Access
- Public
- Created
- Sep 23, 2026
- Updated
- Sep 23, 2026
- SHA
- 8fe0e1bb
Model card
From the Hugging Face model README
PPO LunarLander Agent
This repository contains a PPO (Proximal Policy Optimization) agent implemented in PyTorch and trained on the LunarLander environment as part of the Hugging Face Deep Reinforcement Learning Course — Unit 8 Part I.
Algorithm
- PPO (Proximal Policy Optimization)
- Actor-Critic architecture
- Generalized Advantage Estimation (GAE)
- PPO clipped objective
- PyTorch
Environment
- Environment:
LunarLander-v3 - Observation space: 8
- Action space: 4
Training Hyperparameters
- Learning rate: 2.5e-4
- Gamma: 0.99
- GAE lambda: 0.95
- PPO clip coefficient: 0.2
- Number of environments: 8
- Rollout steps: 128
- PPO epochs: 4
- Minibatch size: 256
- Entropy coefficient: 0.01
- Value function coefficient: 0.5
- Gradient clipping: 0.5
Evaluation
Evaluation was performed over 10 episodes.
Mean reward: -132.37 +/- 43.43
Files
model.pt— trained PPO modelresults.json— evaluation resultsreplay.mp4— agent gameplay replay
Course
Hugging Face Deep Reinforcement Learning Course
Unit 8 Part I — PPO with PyTorch.
- Task
- Reinforcement Learning
- Created
- Sep 23, 2026
- Updated
- Sep 23, 2026