Efficient Reward Identification In Max Entropy Reinforcement Learning with Sparsity and Rank Priors
This work addresses reward identification for reinforcement learning practitioners, offering incremental improvements through structured priors.
The paper tackles the ill-posed problem of recovering time-varying reward functions from optimal policies or demonstrations in max entropy reinforcement learning by introducing sparsity and rank priors, resulting in efficient polynomial-time algorithms that accurately recover rewards with demonstrated generalizability.
In this paper, we consider the problem of recovering time-varying reward functions from either optimal policies or demonstrations coming from a max entropy reinforcement learning problem. This problem is highly ill-posed without additional assumptions on the underlying rewards. However, in many applications, the rewards are indeed parsimonious, and some prior information is available. We consider two such priors on the rewards: 1) rewards are mostly constant and they change infrequently, 2) rewards can be represented by a linear combination of a small number of feature functions. We first show that the reward identification problem with the former prior can be recast as a sparsification problem subject to linear constraints. Moreover, we give a polynomial-time algorithm that solves this sparsification problem exactly. Then, we show that identifying rewards representable with the minimum number of features can be recast as a rank minimization problem subject to linear constraints, for which convex relaxations of rank can be invoked. In both cases, these observations lead to efficient optimization-based reward identification algorithms. Several examples are given to demonstrate the accuracy of the recovered rewards as well as their generalizability.