REINFORCEMENT LEARNING FOR COORDINATED REACTIVE CONTROL

The demands of rapid response and the complexity of many environments make it diicult to decompose, tune and coordinate reactive behaviors while ensuring consistency. Reinforcement learning networks can address the tuning problem, but do not address the problem of decomposition and coordination. We hypothesize that interacting reactions can often be decomposed into separate control tasks resident in separate networks and that the interaction can be coordinated through the tuning mechanism and a higher level controller. To explore these issues, we have implemented a reinforcement learning architecture as the reactive component of a two layer control system for a simulated race car. By varying the architecture, we test whether decomposing reactivity into separate controllers leads to superior overall performance and learning convergence in our domain.