2 citations · 2 across the 8 of their papers we have counts for
8 papers
Foundations of Reinforcement Learning and Control:Connections and New Perspectives
Claire Vernade, Onno Eberhard, Martha White +4
Reinforcement learning and control theory are two adjacent scientific fields that focus on optimizing the controller of unknown dynamical systems using feedback. While both fields…
Deconstructing Actor-Critic: A Large-scale Empirical Study of Design Components for Practitioners
Haseeb Shah, Lingwei Zhu, Adam White +1
Reinforcement learning is increasingly being considered for controlling real-world systems, from fusion plasma and autonomous vehicles to drug discovery and drinking water treatmen…
Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models
Pranaya Jajoo, Harshit Sikchi, Siddhant Agarwal +3
Behavioral Foundation Models (BFMs) produce agents with the capability to adapt to any unknown reward or task. These methods, however, are only able to produce near-optimal policie…
Value Bonuses using Ensemble Errors for Exploration in Reinforcement Learning
Abdul Wahab, Raksha Kumaraswamy, Martha White
Optimistic value estimates provide one mechanism for directed exploration in reinforcement learning (RL). The agent acts greedily with respect to an estimate of the value plus what…
Symmetric Behavior Regularized Policy Optimization
Lingwei Zhu, Haseeb Shah, Zheng Chen +1
Behavior Regularized Policy Optimization (BRPO) leverages asymmetric divergence regularization to mitigate distribution shift in offline reinforcement learning. This paper is the f…
PC-Gym: Benchmark Environments For Process Control Problems
Maximilian Bloor, José Torraca, Ilya Orson Sandoval +6
PC-Gym is an open-source tool for developing and evaluating reinforcement learning (RL) algorithms in chemical process control. It features environments that simulate various chemi…
q-exponential family for policy optimization
Lingwei Zhu, Haseeb Shah, Han Wang +2
Policy optimization methods benefit from a simple and tractable policy parametrization, usually the Gaussian for continuous action spaces. In this paper, we consider a broader poli…
What to Do When Your Discrete Optimization Is the Size of a Neural Network?
Hugo Silva, Martha White
Oftentimes, machine learning applications using neural networks involve solving discrete optimization problems, such as in pruning, parameter-isolation-based continual learning and…