A note on genetic algorithms for large-scale feature selection
Pattern Recognition Letters
Instance-Based Learning Algorithms
Machine Learning
Technical Note: \cal Q-Learning
Machine Learning
Temporal difference learning and TD-Gammon
Communications of the ACM
Artificial life meets entertainment: lifelike autonomous agents
Communications of the ACM
Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Reinforced Genetic Programming
Genetic Programming and Evolvable Machines
Multi-Agent Patrolling with Reinforcement Learning
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 3
Hierarchical reinforcement learning: a hybrid approach
Hierarchical reinforcement learning: a hybrid approach
Reinforcement Learning Hierarchical Neuro-Fuzzy Politree Model for Control of Autonomous Agents
HIS '04 Proceedings of the Fourth International Conference on Hybrid Intelligent Systems
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
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This paper presents a novel hybrid learning method and performance evaluation methodology for adaptive autonomous agents. Measuring the performance of a learning agent is not a trivial task and generally requires long simulations as well as knowledge about the domain. A generic evaluation methodology has been developed to precisely evaluate the performance of policy estimation techniques. This methodology has been integrated into a hybrid learning algorithm which aim is to decrease the learning time and the amount of errors of an adaptive agent. The hybrid learning method namely K-learning, integrates the Q-learning and K Nearest-Neighbors algorithm. Experiments show that the K-learning algorithm surpasses the Q-learning algorithm in terms of convergence speed to a good policy.