275 citations · 341 across the 6 of their papers we have counts for
9 papers
Optimizing Industrial HVAC Systems with Hierarchical Reinforcement Learning
William Wong, Praneet Dutta, Octavian Voicu +3
Reinforcement learning (RL) techniques have been developed to optimize industrial cooling systems, offering substantial energy savings compared to traditional heuristic policies. A…
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction Estimation
Jongmin Lee, Cosmin Paduraru, Daniel J. Mankowitz +4
We consider the offline constrained reinforcement learning (RL) problem, in which the agent aims to compute a policy that maximizes expected return while satisfying given cost cons…
Autoregressive Dynamics Models for Offline Policy Evaluation and Optimization
Michael R. Zhang, Tom Le Paine, Ofir Nachum +4
Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action…
Benchmarks for Deep Off-Policy Evaluation
Justin Fu, Mohammad Norouzi, Ofir Nachum +10
Off-policy evaluation (OPE) holds the promise of being able to leverage large, offline datasets for both evaluating and selecting complex policies for decision making. The ability…
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification
Daniel J. Mankowitz, Dan A. Calian, Rae Jeong +5
Many real-world physical control systems are required to satisfy constraints upon deployment. Furthermore, real-world systems are often subject to effects such as non-stationarity,…
Hyperparameter Selection for Offline Reinforcement Learning
Tom Le Paine, Cosmin Paduraru, Andrea Michi +5
Offline reinforcement learning (RL purely from logged data) is an important avenue for deploying RL techniques in real-world scenarios. However, existing hyperparameter selection m…