Generating interpretable reinforcement learning policies using genetic programming

The search for interpretable reinforcement learning policies is of high academic and industrial interest. Especially for industrial systems, domain experts are more likely to deploy autonomously learned controllers if they are understandable and convenient to evaluate. Basic algebraic equations are supposed to meet these requirements, as long as they are restricted to an adequate complexity. In our recent work "Interpretable policies for reinforcement learning by genetic programming" published in Engineering Applications of Artificial Intelligence 76 (2018), we introduced the genetic programming for reinforcement learning (GPRL) approach. GPRL uses model-based batch reinforcement learning and genetic programming and autonomously learns policy equations from preexisting default state-action trajectory samples. Experiments on three reinforcement learning benchmarks demonstrate that GPRL can produce human-interpretable policies of high control performance.