Probabilistic inference for solving discrete and continuous state Markov Decision Processes
ICML '06 Proceedings of the 23rd international conference on Machine learning
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Control in the natural environment is difficult in part because of uncertainty in the effect of actions. Uncertainty can be due to added motor or sensory noise, unmodeled dynamics, or quantization of sensory feedback. Biological systems are faced with further difficulties, since control must be performed by networks of cooperating neurons and neural subsystems. Here, we propose a new mathematical framework for modeling and simulation of distributed control systems operating in an uncertain environment. Stochastic differential operators can be derived from the stochastic differential equation describing a system, and they map the current state density into the differential of the state density. Unlike discrete-time Markov update operators, stochastic differential operators combine linearly for a large class of linear and nonlinear systems, and therefore the combined effects of multiple controllable and uncontrollable subsystems can be predicted. Design using these operators yields systems whose statistical behavior can be specified throughout state-space. The relationship to Bayesian estimation and discrete-time Markov processes is described.