PPO Deep Reinforcement Learning Control Example

PPO DRL continuous control example with customized environment based on Deep Learning Toolbox

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This fileexchange provides a clean, modular implementation of the Proximal Policy Optimization (PPO) algorithm with clipping (PPO‑Clip) using MATLAB® and the Deep Learning Toolbox™. It is tailored for continuous action spaces and can be easily adapted to any custom environment by simply replacing the environment functions.
The core algorithm is built entirely with dlnetwork objects, enabling automatic differentiation, GPU acceleration, and full compatibility with custom training loops.

Cite As

Chuguang Pan (2026). PPO Deep Reinforcement Learning Control Example (https://www.mathworks.com/matlabcentral/fileexchange/183907-ppo-deep-reinforcement-learning-control-example), MATLAB Central File Exchange. Retrieved .

General Information

MATLAB Release Compatibility

  • Compatible with any release

Platform Compatibility

  • Windows
  • macOS
  • Linux
Version Published Release Notes Action
1.0.1

Add some comments for clarification

1.0.0