Reinforcement Learning as Software Engineering

A central tenant of reinforcement learning (RL) is that behavior is driven by a desire to maximize rewarding stimuli. In the computing context, RL can be seen as a software engineering methodology for specifying the behavior of agents in complex, uncertain environments. In this analogy, Markov Decision Processes– especially an MDP’s rewards–are programs while learning algorithms are compilers. In general, the field has focused almost exclusively on the compilers—the design of algorithms for finding reward-maximizing behavior—but not much attention has been paid to the role of the programming language and the software engineering support for helping developers build good programs. In this talk, I will describe our efforts to probe the nature of MDPs-as-programs with the goals of moving toward higher-level specifications that satisfy the software engineering goals of clear semantics, expressibility, and ease of use while still admitting the efficient compilers that the RL community has traditionally enjoyed. Poster Session 1, Monday, June 8, 2015 Starred posters will also give a plenary talk.

Authors

• Charles Isbell

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