Noise strategies for improving local search
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Reactive search, a history-sensitive heuristic for MAX-SAT
Journal of Experimental Algorithmics (JEA)
Learning evaluation functions for global optimization and Boolean satisfiability
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Neuro-Dynamic Programming
Algorithm Selection using Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
Scaling and Probabilistic Smoothing: Efficient Dynamic Local Search for SAT
CP '02 Proceedings of the 8th International Conference on Principles and Practice of Constraint Programming
KI '97 Proceedings of the 21st Annual German Conference on Artificial Intelligence: Advances in Artificial Intelligence
Least-squares policy iteration
The Journal of Machine Learning Research
Reactive Search and Intelligent Optimization
Reactive Search and Intelligent Optimization
The exponentiated subgradient algorithm for heuristic Boolean programming
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A reinforcement learning approach to job-shop scheduling
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Reinforcement learning for online control of evolutionary algorithms
ESOA'06 Proceedings of the 4th international conference on Engineering self-organising systems
Evidence for invariants in local search
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Instance-Based parameter tuning via search trajectory similarity clustering
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
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This paper is about Reinforcement Learning (RL) applied to online parameter tuning in Stochastic Local Search (SLS) methods. In particular a novel application of RL is considered in the Reactive Tabu Search (RTS) method, where the appropriate amount of diversification in prohibition-based (Tabu) local search is adapted in a fast online manner to the characteristics of a task and of the local configuration. We model the parameter-tuning policy as a Markov Decision Process where the states summarize relevant information about the recent history of the search, and we determine a near-optimal policy by using the Least Squares Policy Iteration (LSPI) method. Preliminary experiments on Maximum Satisfiability (MAX-SAT) instances show very promising results indicating that the learnt policy is competitive with previously proposed reactive strategies.