3 citations · 9 across the 6 of their papers we have counts for
6 papers
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…
From Past to Future: Rethinking Eligibility Traces
Dhawal Gupta, Scott M. Jordan, Shreyas Chaudhari +3
In this paper, we introduce a fresh perspective on the challenges of credit assignment and policy evaluation. First, we delve into the nuances of eligibility traces and explore ins…
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…
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…
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…
Enforcing Delayed-Impact Fairness Guarantees
Aline Weber, Blossom Metevier, Yuriy Brun +2
Recent research has shown that seemingly fair machine learning models, when used to inform decisions that have an impact on peoples' lives or well-being (e.g., applications involvi…