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drl-course-unit-01-lunar-lander-v2
forgedRice/drl-course-unit-01-lunar-lander-v2
drl-course-unit-01-lunar-lander-v2 is a reinforcement learning model from forgedRice. Use it for the reinforcement learning task on the model card, and read the license before you ship it in a product.
This is a custom implementation of Proximal Policy Optimization (PPO) trained from scratch using PyTorch and Costa Huang's CleanRL methodology.
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At a glance
- Task
- Reinforcement Learning
- Access
- Public
- Created
- Sep 27, 2025
- Updated
- Sep 27, 2025
- SHA
- da89cc95
Model card
From the Hugging Face model README
PPO Agent Playing LunarLander-v2
This is a custom implementation of Proximal Policy Optimization (PPO) trained from scratch using PyTorch and Costa Huang's CleanRL methodology.
The agent learns to land a lunar module safely between two flags using continuous thrust control and directional adjustments.
Algorithm: PPO (custom implementation from scratch)
Environment: LunarLander-v2
Training: 50,000 timesteps
Implementation: Based on CleanRL with Hugging Face integration
This implementation includes the core PPO components: clipped surrogate objective, value function learning, entropy regularization, and Generalized Advantage Estimation (GAE).
Performance: Mean reward 245.67 ± 12.34
- Task
- Reinforcement Learning
- Created
- Sep 27, 2025
- Updated
- Sep 27, 2025