Biology: Animal Behavior, Biology: Ethology, Computer Science: Artificial Intelligence, Computer Science: Dynamical Systems, Computer Science: Machine Learning, Computer Science: Robotics, Animal Behavior, Ethology, Artificial Intelligence, Dynamical Systems, Machine Learning, Robotics

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.