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Nate Rahn

Doctorat - McGill
Superviseur⋅e principal⋅e
Marc Gendron-Bellemare
Co-supervisor
Doina Precup
Sujets de recherche
Apprentissage par renforcement
Apprentissage profond

Publications

Policy Optimization in a Noisy Neighborhood: On Return Landscapes in Continuous Control
Nate Rahn
Pierluca D'Oro
Harley Wiltzer
Pierre-Luc Bacon
Bellemare Marc-Emmanuel
Deep reinforcement learning agents for continuous control are known to exhibit significant instability in their performance over time. In th… (voir plus)is 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 algorithms traverse noisy neighborhoods of this landscape, in which a single update to the policy parameters leads to a wide range of returns. By taking a distributional view of these returns, we map the landscape, characterizing failure-prone regions of policy space and revealing a hidden dimension of policy quality. We show that the landscape exhibits surprising structure by finding simple paths in parameter space which improve the stability of a policy. To conclude, we develop a distribution-aware procedure which finds such paths, navigating away from noisy neighborhoods in order to improve the robustness of a policy. Taken together, our results provide new insight into the optimization, evaluation, and design of agents.
2023-09-20
NeurIPS.cc/2023/Conference (poster)
doi.org
openreview.net