Reviews: A neurally plausible model learns successor representations in partially observable environments

This work proposes a neurally plausible approach to reinforcement learning in partially-observed MDPs based on distributional successor features. The approach allows for efficient value function computation as demonstrated empirically. The three expert reviewers were unanimous that this paper should be accepted, and I see no reason to contradict their opinions.