Reinforcement Learning as a Context for Integrating AI Research

Bill Hibbard

One key to intelligent brain design may be a set of interacting reinforcement learning processes, with different reinforcement values and with prior biases on their learning derived from various AI research fields. Values for predictive accuracy and biases derived from collective human knowledge may be useful for processes that learn to simulate the world and help to solve the credit assignment problem for other processes whose values express the brain’s goals in the world. An intelligent brain design will also require processes that memorize sensory information and internal representations.

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