Portrait de Harley Wiltzer

Harley Wiltzer

Doctorat - McGill
Superviseur⋅e principal⋅e
David Meger
Co-supervisor
Marc Gendron-Bellemare
Sujets de recherche
Apprentissage par renforcement
Modèles probabilistes
Systèmes dynamiques
Site web
Google Scholar
GitHub

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
Distributional Hamilton-Jacobi-Bellman Equations for Continuous-Time Reinforcement Learning
Harley Wiltzer
David Meger
Bellemare Marc-Emmanuel
Continuous-time reinforcement learning offers an appealing formalism for describing control problems in which the passage of time is not nat… (voir plus)urally divided into discrete increments. Here we consider the problem of predicting the distribution of returns obtained by an agent interacting in a continuous-time, stochastic environment. Accurate return predictions have proven useful for determining optimal policies for risk-sensitive control, learning state representations, multiagent coordination, and more. We begin by establishing the distributional analogue of the Hamilton-Jacobi-Bellman (HJB) equation for Itô diffusions and the broader class of Feller-Dynkin processes. We then specialize this equation to the setting in which the return distribution is approximated by
2022-06-27
Proceedings of the 39th International Conference on Machine Learning (publié)
doi.org
proceedings.mlr.press