activity
20192021
most citedLearning Action Representations for Reinforcement Learning

22 citations · 32 across the 4 of their papers we have counts for

collaborators

7 papers

cs.LG2021

Multiscale Manifold Warping

Sridhar Mahadevan, Anup Rao, Georgios Theocharous +1

Many real-world applications require aligning two temporal sequences, including bioinformatics, handwriting recognition, activity recognition, and human-robot coordination. Dynamic…

cs.LG2020

Towards Safe Policy Improvement for Non-Stationary MDPs

Yash Chandak, Scott M. Jordan, Georgios Theocharous +2

Many real-world sequential decision-making problems involve critical systems with financial risks and human-life risks. While several works in the past have proposed methods that a…

cs.LG20202 cited

Reinforcement Learning for Strategic Recommendations

Georgios Theocharous, Yash Chandak, Philip S. Thomas +1

Strategic recommendations (SR) refer to the problem where an intelligent agent observes the sequential behaviors and activities of users and decides when and how to interact with t…

cs.LG20208 cited

Optimizing for the Future in Non-Stationary MDPs

Yash Chandak, Georgios Theocharous, Shiv Shankar +3

Most reinforcement learning methods are based upon the key assumption that the transition dynamics and reward functions are fixed, that is, the underlying Markov decision process i…

cs.LG2019

Reinforcement Learning When All Actions are Not Always Available

Yash Chandak, Georgios Theocharous, Blossom Metevier +1

The Markov decision process (MDP) formulation used to model many real-world sequential decision making problems does not efficiently capture the setting where the set of available…

cs.LG2019

Lifelong Learning with a Changing Action Set

Yash Chandak, Georgios Theocharous, Chris Nota +1

In many real-world sequential decision making problems, the number of available actions (decisions) can vary over time. While problems like catastrophic forgetting, changing transi…