Modern heuristic techniques for combinatorial problems
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Learning to Predict by the Methods of Temporal Differences
Machine Learning
On the Relationship between Learning Capability and the Boltzmann-Formula
Proceedings of the 14th International conference on Industrial and engineering applications of artificial intelligence and expert systems: engineering of intelligent systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Simulation and reinforcement learning with soccer agents
Multiagent and Grid Systems - Innovations in intelligent agent technology
Heuristic search based exploration in reinforcement learning
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
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
Convergence analysis on approximate reinforcement learning
KSEM'07 Proceedings of the 2nd international conference on Knowledge science, engineering and management
A new Q-learning algorithm based on the metropolis criterion
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Research Directions in the KES Centre
RSCTC '08 Proceedings of the 6th International Conference on Rough Sets and Current Trends in Computing
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The trade-off between exploration and exploitation has an important impact on the performance of temporal difference learning. There are several action selection strategies, however, it is unclear which strategy is better. The impact of action selection strategies may depend on the application domains and human factors. This paper presents a modified Sarsa(λ) control algorithm by sampling actions in conjunction with simulated annealing technique. A game of soccer is utilised as the simulation environment, which has a large, dynamic and continuous state space. The empirical results demonstrate that the quality of convergence has been significantly improved by using the simulated annealing approach.