46 citations · 61 across the 9 of their papers we have counts for
8 papers · 1 filter
Position: Benchmarking is Limited in Reinforcement Learning Research
Scott M. Jordan, Adam White, Bruno Castro da Silva +2
Novel reinforcement learning algorithms, or improvements on existing ones, are commonly justified by evaluating their performance on benchmark environments and are compared to an e…
Behavior Alignment via Reward Function Optimization
Dhawal Gupta, Yash Chandak, Scott M. Jordan +2
Designing reward functions for efficiently guiding reinforcement learning (RL) agents toward specific behaviors is a complex task. This is challenging since it requires the identif…
Learning Fair Representations with High-Confidence Guarantees
Yuhong Luo, Austin Hoag, Philip S. Thomas
Representation learning is increasingly employed to generate representations that are predictive across multiple downstream tasks. The development of representation learning algori…
Coagent Networks: Generalized and Scaled
James E. Kostas, Scott M. Jordan, Yash Chandak +5
Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011] provide a powerful and flexible framework for deriving principled learning rules for arbitrary stochastic…
Optimization using Parallel Gradient Evaluations on Multiple Parameters
Yash Chandak, Shiv Shankar, Venkata Gandikota +2
We propose a first-order method for convex optimization, where instead of being restricted to the gradient from a single parameter, gradients from multiple parameters can be used d…
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments
Yash Chandak, Shiv Shankar, Nathaniel D. Bastian +3
Methods for sequential decision-making are often built upon a foundational assumption that the underlying decision process is stationary. This limits the application of such method…