Nathan Rahn

Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control

Deep reinforcement learning agents for continuous control are known to exhibit significant instability in their performance over time. In this work, we provide a fresh perspective on these behaviors by studying the return landscape: the mapping between a policy and a return. We find that popular alg…