Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Machine Learning - Special issue on robot learning
Learning hierarchical control structures for multiple tasks and changing environments
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Robot Learning
Discovering Hierarchy in Reinforcement Learning with HEXQ
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Team-Partitioned, Opaque-Transition Reinforced Learning
RoboCup-98: Robot Soccer World Cup II
Generating hierarchical structure in reinforcement learning from state variables
PRICAI'00 Proceedings of the 6th Pacific Rim international conference on Artificial intelligence
Autonomous discovery of subgoals using acyclic state trajectories
ICICA'10 Proceedings of the First international conference on Information computing and applications
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One of the most formidable issues of RL application to real robot tasks is how to find a suitable state space, and this has been much more serious since recent robots tends to have more sensors and the environment including other robots becomes more complicated. In order to cope with the issue, this paper presents a method of self task decomposition for modular learning system based on self-interpretation of instructions given by a coach. The proposed method is applied to a simple soccer situation in the context of RoboCup.