K-spin Hamiltonian for quantum-resolvable Markov decision processes

13 Apr 2020Eric B. JonesPeter GrafEliot KapitWesley Jones

The Markov decision process is the mathematical formalization underlying the modern field of reinforcement learning when transition and reward functions are unknown. We derive a pseudo-Boolean cost function that is equivalent to a K-spin Hamiltonian representation of the discrete, finite, discounted Markov decision process with infinite horizon... (read more)

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