Reviews: A Deep Bayesian Policy Reuse Approach Against Non-Stationary Agents

The paper focuses on an important problem in multiagent learning - non-stationarity introduced by other agents. It proposes a novel rectified belief model to overcome the problem of indistinguishability with miscoordinated policies and combines a few ideas made popular by neural networks - sharing weights and distillation. This results in an extension of the idea of Bayesian Policy reuse, originally formulated for transfer learning and later extended into BPR for online learning, which the paper terms Deep BPR . The paper tests the efficacy of their approach on relatively small tasks and finds that the proposed method can perform quite close to an omniscient one. The paper clearly traces the origin of its ideas to BPR and BPR algorithms and the limitations it's trying to overcome.