Scott M. Jordan

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 18

Top Topics

High Confidence

Markov Decision Process

Reward Function

Reinforcement Learning

Top Venues

Publications

Scott M. Jordan, Adam White, Bruno Castro da Silva, Martha White, Philip S. Thomas
Position: Benchmarking is Limited in Reinforcement Learning Research. CoRR (2024)
Dhawal Gupta, Scott M. Jordan, Shreyas Chaudhari, Bo Liu, Philip S. Thomas, Bruno Castro da Silva
From Past to Future: Rethinking Eligibility Traces. AAAI (2024)
Kevin Roice, Parham Mohammad Panahi, Scott M. Jordan, Adam White, Martha White
A New View on Planning in Online Reinforcement Learning. CoRR (2024)
James E. Kostas, Scott M. Jordan, Yash Chandak, Georgios Theocharous, Dhawal Gupta, Martha White, Bruno Castro da Silva, Philip S. Thomas
Coagent Networks: Generalized and Scaled. CoRR (2023)
Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas, Bruno C. da Silva
Behavior Alignment via Reward Function Optimization. NeurIPS (2023)
Dhawal Gupta, Scott M. Jordan, Shreyas Chaudhari, Bo Liu, Philip S. Thomas, Bruno Castro da Silva
From Past to Future: Rethinking Eligibility Traces. CoRR (2023)
Wenhao Yang, Han Wang, Tadashi Kozuno, Scott M. Jordan, Zhihua Zhang
Avoiding Model Estimation in Robust Markov Decision Processes with a Generative Model. CoRR (2023)
Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas, Bruno Castro da Silva
Behavior Alignment via Reward Function Optimization. CoRR (2023)
James Kostas, Yash Chandak, Scott M. Jordan, Georgios Theocharous, Philip S. Thomas
High Confidence Generalization for Reinforcement Learning. ICML (2021)
Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas
Towards Safe Policy Improvement for Non-Stationary MDPs. CoRR (2020)
Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas
Evaluating the Performance of Reinforcement Learning Algorithms. CoRR (2020)
Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas
Evaluating the Performance of Reinforcement Learning Algorithms. ICML (2020)
Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas
Towards Safe Policy Improvement for Non-Stationary MDPs. NeurIPS (2020)
Philip S. Thomas, Scott M. Jordan, Yash Chandak, Chris Nota, James Kostas
Classical Policy Gradient: Preserving Bellman's Principle of Optimality. CoRR (2019)
Yash Chandak, Georgios Theocharous, James Kostas, Scott M. Jordan, Philip S. Thomas
Learning Action Representations for Reinforcement Learning. CoRR (2019)
Daniel Cohen, Scott M. Jordan, W. Bruce Croft
Learning a Better Negative Sampling Policy with Deep Neural Networks for Search. ICTIR (2019)
Yash Chandak, Georgios Theocharous, James Kostas, Scott M. Jordan, Philip S. Thomas
Learning Action Representations for Reinforcement Learning. ICML (2019)
Daniel Cohen, Scott M. Jordan, W. Bruce Croft
Distributed Evaluations: Ending Neural Point Metrics. CoRR (2018)