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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: 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 |