Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer
RoboCup 2000: Robot Soccer World Cup IV
Karlsruhe Brainstormers - A Reinforcement Learning Approach to Robotic Soccer
RoboCup 2001: Robot Soccer World Cup V
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents
RoboCup 2007: Robot Soccer World Cup XI
Pareto-Optimal Offensive Player Positioning in Simulated Soccer
RoboCup 2007: Robot Soccer World Cup XI
Evolving Static Representations for Task Transfer
The Journal of Machine Learning Research
Case-Based Multiagent Reinforcement Learning: Cases as Heuristics for Selection of Actions
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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We present half field offense, a novel subtask of RoboCup simulated soccer, and pose it as a problem for reinforcement learning. In this task, an offense team attempts to outplay a defense team in order to shoot goals. Half field offense extends keepaway [11], a simpler subtask of RoboCup soccer in which one team must try to keep possession of the ball within a small rectangular region, and away from the opposing team. Both keepaway and half field offense have to cope with the usual problems of RoboCup soccer, such as a continuous state space, noisy actions, and multiple agents, but the latter is a significantly harder multiagent reinforcement learning problem because of sparse rewards, a larger state space, a richer action set, and the sheer complexity of the policy to be learned. We demonstrate that the algorithm that has been successful for keepaway is inadequate to scale to the more complex half field offense task, and present a new algorithm to address the aforementioned problems in multiagent reinforcement learning. The main feature of our algorithm is the use of inter-agent communication, which allows for more frequent and reliable learning updates. We show empirical results verifying that our algorithm registers significantly higher performance and faster learning than the earlier approach. We also assess the contribution of inter-agent communication by considering several variations of the basic learning method. This work is a step further in the ongoing challenge to learn complete team behavior for the RoboCup simulated soccer task.