TY - GEN ID - cogprints447 UR - http://cogprints.org/447/ A1 - Humphrys, Mark Y1 - 1996/// N2 - Action Selection schemes, when translated into precise algorithms, typically involve considerable design effort and tuning of parameters. Little work has been done on solving the problem using learning. This paper compares eight different methods of solving the action selection problem using Reinforcement Learning (learning from rewards). The methods range from centralised and cooperative to decentralised and selfish. They are tested in an artificial world and their performance, memory requirements and reactiveness are compared. Finally, the possibility of more exotic, ecosystem-like decentralised models are considered. PB - MIT Press/Bradford Books TI - Action Selection methods using Reinforcement Learning SP - 135 AV - public EP - 144 ER -