[R] Supercharging reinforcement learning with logic

Deep reinforcement learning has led to a variety of compelling results. However, performance issues, particularly relating to the data efficiency of simulation has limited it applicability in domains where simulations run more slowly. Our solution is to use a logic base framework, PyReason, as a proxy for the simulation.

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We showed that inference with PyReason logic program can provide up to a three order-of-magnitude speedup when compared with native simulations (we studied AFSIM and Starcraft2) while providing comparable …

data domains efficiency framework inference logic machinelearning performance reinforcement reinforcement learning simulation simulations solution the simulation