Robust Asymmetric Learning in POMDPs

Policies for partially observed Markov decision processes can be efficiently learned by imitating expert policies learned using asymmetric information. Unfortunately, existing approaches for this kind of imitation learning have a serious flaw: the expert does not know what the trainee cannot see, and may therefore encourage actions that are sub-optimal or unsafe under partial information. To address this flaw, we derive an update that, when applied iteratively to an expert, maximizes the expected reward of the trainee's policy. Using this update, we construct a computationally efficient algorithm, adaptive asymmetric DAgger (A2D), that jointly trains the expert and trainee policies. We then show that A2D allows the trainee to safely imitate the modified expert, and outperforms policies learned either by imitating a fixed expert or direct reinforcement learning.