Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Scaling Reinforcement Learning toward RoboCup Soccer
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Classifier prediction based on tile coding
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Fuzzy and tile coding function approximation in agent coevolution
AIA'06 Proceedings of the 24th IASTED international conference on Artificial intelligence and applications
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Empirical Studies in Action Selection with Reinforcement Learning
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Tile Coding Based on Hyperplane Tiles
Recent Advances in Reinforcement Learning
Simulation and reinforcement learning with soccer agents
Multiagent and Grid Systems - Innovations in intelligent agent technology
Experimental analysis on Sarsa(λ) and Q(λ) with different eligibility traces strategies
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology - Theoretical advances of intelligent paradigms
Reinforcement distribution in fuzzy Q-learning
Fuzzy Sets and Systems
Fuzzy CMAC with automatic state partition for reinforcementlearning
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Representation transfer via elaboration
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
A case study on the critical role of geometric regularity in machine learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Reinforcement learning of competitive skills with soccer agents
KES'07/WIRN'07 Proceedings of the 11th international conference, KES 2007 and XVII Italian workshop on neural networks conference on Knowledge-based intelligent information and engineering systems: Part I
Continuous-state reinforcement learning with fuzzy approximation
ALAMAS'05/ALAMAS'06/ALAMAS'07 Proceedings of the 5th , 6th and 7th European conference on Adaptive and learning agents and multi-agent systems: adaptation and multi-agent learning
Multiple overlapping tiles for contextual monte carlo tree search
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Reinforcement Learning with Reward Shaping and Mixed Resolution Function Approximation
International Journal of Agent Technologies and Systems
Learning via human feedback in continuous state and action spaces
Applied Intelligence
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Reinforcement learning (RL) is a powerful abstraction of sequential decision making that has an established theoretical foundation and has proven effective in a variety of small, simulated domains. The success of RL on real-world problems with large, often continuous state and action spaces hinges on effective function approximation. Of the many function approximation schemes proposed, tile coding strikes an empirically successful balance among representational power, computational cost, and ease of use and has been widely adopted in recent RL work. This paper demonstrates that the performance of tile coding is quite sensitive to parameterization. We present detailed experiments that isolate the effects of parameter choices and provide guidance to their setting. We further illustrate that no single parameterization achieves the best performance throughout the learning curve, and contribute an automated technique for adjusting tile-coding parameters online. Our experimental findings confirm the superiority of adaptive parameterization to fixed settings. This work aims to automate the choice of approximation scheme not only on a problem basis but also throughout the learning process, eliminating the need for a substantial tuning effort.