Distilling Deep RL Models Into Interpretable Neuro-Fuzzy Systems. (arXiv:2209.03357v1 [cs.LG])

Deep Reinforcement Learning uses a deep neural network to encode a policy,
which achieves very good performance in a wide range of applications but is
widely regarded as a black box model. A more interpretable alternative to deep
networks is given by neuro-fuzzy controllers. Unfortunately, neuro-fuzzy
controllers often need a large number of rules to solve relatively simple
tasks, making them difficult to interpret. In this work, we present an
algorithm to distill the policy from a deep Q-network into …

arxiv deep rl neuro systems