Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Keepaway Soccer: A Machine Learning Testbed
RoboCup 2001: Robot Soccer World Cup V
Evolving Soccer Keepaway Players Through Task Decomposition
Machine Learning
Transfer via inter-task mappings in policy search reinforcement learning
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Temporal difference and policy search methods for reinforcement learning: an empirical comparison
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 2
Using advice to transfer knowledge acquired in one reinforcement learning task to another
ECML'05 Proceedings of the 16th European conference on Machine Learning
Multi-agent, reward shaping for RoboCup KeepAway
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
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Keepaway is a sub-problem of RoboCup Soccer Simulator in which 'the keepers' try to maintain the possession of the ball, while 'the takers' try to steal the ball or force it out of bounds. By using Reinforcement Learning as a learning method, a lot of research has been done in this domain. In these works, there has been a remarkable success for the intelligent keepers part, however most of these keepers are trained and tested against simple hand-coded takers. We tried to address this part of the problem by using Sarsa(λ) as a Reinforcement Learning method with linear tile-coding as function approximation and used two different state spaces that we specially designed for the takers. As the results of the experiments confirm, we outperformed the hand-coded taker which results in creating a better trainer and tester for the keepers. Also when designing the new state space, we noticed that smaller state spaces can also be successful for this part of the problem.