Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
State abstraction for programmable reinforcement learning agents
Eighteenth national conference on Artificial intelligence
Temporal credit assignment in reinforcement learning
Temporal credit assignment in reinforcement learning
Programmable reinforcement learning agents
Programmable reinforcement learning agents
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Using Machine Learning to Focus Iterative Optimization
Proceedings of the International Symposium on Code Generation and Optimization
Compilers: Principles, Techniques, and Tools (2nd Edition)
Compilers: Principles, Techniques, and Tools (2nd Edition)
On the difficulty of modular reinforcement learning for real-world partial programming
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Concurrent hierarchical reinforcement learning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Reusable, generic program analyses and transformations
GPCE '09 Proceedings of the eighth international conference on Generative programming and component engineering
Adaptation-based programming in java
Proceedings of the 20th ACM SIGPLAN workshop on Partial evaluation and program manipulation
Robust Learning for Adaptive Programs by Leveraging Program Structure
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
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In the adaptation-based programming (ABP) paradigm, programs may contain variable parts (function calls, parameter values, etc.) that can be take a number of different values. Programs also contain reward statements with which a programmer can provide feedback about how well a program is performing with respect to achieving its goals (for example, achieving a high score on some scale). By repeatedly running the program, a machine learning component will, guided by the rewards, gradually adjust the automatic choices made in the variable program parts so that they converge toward an optimal strategy. ABP is a method for semi-automatic program generation in which the choices and rewards offered by programmers allow standard machine-learning techniques to explore a design space defined by the programmer to find an optimal instance of a program template. ABP effectively provides a DSL that allows non-machine-learning experts to exploit machine learning to generate self-optimizing programs. Unfortunately, in many cases the placement and structuring of choices and rewards can have a detrimental effect on how an optimal solution to a program-generation problem can be found. To address this problem, we have developed a dataflow analysis that computes influence tracks of choices and rewards. This information can be exploited by an augmented machine-learning technique to ignore misleading rewards and to generally attribute rewards better to the choices that have actually influenced them. Moreover, this technique allows us to detect errors in the adaptive program that might arise out of program maintenance. Our evaluation shows that the dataflow analysis can lead to improvements in performance.