Adaptation-based programming in java
Proceedings of the 20th ACM SIGPLAN workshop on Partial evaluation and program manipulation
Faster program adaptation through reward attribution inference
Proceedings of the 11th International Conference on Generative Programming and Component Engineering
Learning-Based test programming for programmers
ISoLA'12 Proceedings of the 5th international conference on Leveraging Applications of Formal Methods, Verification and Validation: technologies for mastering change - Volume Part I
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We study how to effectively integrate reinforcement learning (RL) and programming languages via adaptation-based programming, where programs can include non-deterministic structures that can be automatically optimized via RL. Prior work has optimized adaptive programs by defining an induced sequential decision process to which standard RL is applied. Here we show that the success of this approach is highly sensitive to the specific program structure, where even seemingly minor program transformations can lead to failure. This sensitivity makes it extremely difficult for a non-RL-expert to write effective adaptive programs. In this paper, we study a more robust learning approach, where the key idea is to leverage information about program structure in order to define a more informative decision process and to improve the SARSA(\lambda) RL algorithm. Our empirical results show significant benefits for this approach.