Active perception and reinforcement learning
Neural Computation
Integrated systems based on behaviors
ACM SIGART Bulletin
Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Technical Note: \cal Q-Learning
Machine Learning
Recent Advances in Reinforcement Learning
Recent Advances in Reinforcement Learning
Reinforcement Learning in the Multi-Robot Domain
Autonomous Robots
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Hierarchical genetic algorithms operating on populations of computer programs
IJCAI'89 Proceedings of the 11th international joint conference on Artificial intelligence - Volume 1
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
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This paper presents MUTANT, a learning system for autonomous agents. MUTANT is an adaptive control architecture founded on genetic techniques and reinforcement learning. The system allows an agent to learn some complex tasks without requiring its designer to fully specify how they should be carried out. An agent behavior is defined by a set of rules, genetically encoded. The rules are evolved over time by a genetic algorithm to synthesize some new better rules according to their respective adaptive function, computed by progressive reinforcements. The system is validated through an experimentation in collective robotics.