---
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: 245.67 +/- 12.34
      name: mean_reward
      verified: false
---

# 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