Technical Note: \cal Q-Learning
Machine Learning
Average reward reinforcement learning: foundations, algorithms, and empirical results
Machine Learning - Special issue on reinforcement learning
Reinforcement learning with hierarchies of machines
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
The MAXQ Method for Hierarchical Reinforcement Learning
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Learning to Cooperate via Policy Search
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
State abstraction for programmable reinforcement learning agents
Eighteenth national conference on Artificial intelligence
Hierarchical control and learning for markov decision processes
Hierarchical control and learning for markov decision processes
Apprenticeship learning via inverse reinforcement learning
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Towards adaptive programming: integrating reinforcement learning into a programming language
Proceedings of the 23rd ACM SIGPLAN conference on Object-oriented programming systems languages and applications
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments
The Journal of Machine Learning Research
Robust Learning for Adaptive Programs by Leveraging Program Structure
ICMLA '10 Proceedings of the 2010 Ninth International Conference on Machine Learning and Applications
Bandit based monte-carlo planning
ECML'06 Proceedings of the 17th European conference on Machine Learning
Coverage rewarded: Test input generation via adaptation-based programming
ASE '11 Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering
Finding common ground: choose, assert, and assume
Proceedings of the 2012 Workshop on Dynamic Analysis
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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Writing deterministic programs is often difficult for problems whose optimal solutions depend on unpredictable properties of the programs' inputs. Difficulty is also encountered for problems where the programmer is uncertain about how to best implement certain aspects of a solution. For such problems a mixed strategy of deterministic programming and machine learning can often be very helpful: Initially, define those parts of the program that are well understood and leave the other parts loosely defined through default actions, but also define how those actions can be improved depending on results from actual program runs. Then run the program repeatedly and let the loosely defined parts adapt. In this paper we present a library for Java that facilitates this style of programming, called adaptation-based programming. We motivate the design of the library, define the semantics of adaptation-based programming, and demonstrate through two evaluations that the approach works well in practice. Adaptation-based programming is a form of program generation in which the creation of programs is controlled by previous runs. It facilitates a whole new spectrum of programs between the two extremes of totally deterministic programs and machine learning.