Learning to Race: Experiments with a Simulated Race Car
Larry D. Pyeatt, Adele E. Howe · 1998
We have implemented a reinforcement learning architecture as the reactive component of a two layer control system for a simulated race car. We have found that separating the layers has expedited gradually improving competition and multagent interaction. We ran experiments to test the tuning, decomposition and coordination of the low level behaviors. We then extended our control system to allow passing of other cars and tested its ability to avoid collisions. The best design used reinforcement learning with separate networks for each behavior, coarse coded input and a simple rule based coordination mechanism. Introduction Autonomous agents require a mix of behaviors, i.e., responses to different stimuli. This is especially true in situations where there are other agents present or where the environment is otherwise nondeterministic. For an agent to be effective in its environment, it must have a large repertoire of behaviors and must be able to coordinate the use of those ...