An implementation efficient learning algorithm for adaptive control using associative content addressable memory

Three modifications to the Boxes-ASE/ACE reinforcement learning improves implementation efficiency and performance. A state history queue (SHQ) eliminates computations for temporally insignificant states. A dynamic link table only allocates control memory to states the system traverses. CMAC state association uses previous learning to decrease training time. Simulations show a 4-fold improvement in learning. The SHQ in a hardware implementation of the pole-cart balancer reduces computation time 11-fold.<
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