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Description: PolicyBoost is a package for reinforcement learning with boosting-style approaches. The package includes the java code of the algorithms and the demos for the domains including Mountain Car, Acrobot, Corridor World and Helicopter.

References: Yang Yu and Qing Da, PolicyBoost: Functional policy gradient with ranking-based reward objective. In: Proceedings of AAAI Workshop on AI and Robotics (AIRob'14), Quebec City, Canada, 2014.

ATTN: This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Prof. Zhi-Hua Zhou (

Requirement: To use this package, the WEKA environment must be available. Refer: I.H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Francisco, CA, 2000.

ATTN2: This package was developed by Mr. Qing Da ( For any problem concerning the code, please feel free to contact Mr. Da.

Download: code (54KB)
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