22 citations · 62 across the 15 of their papers we have counts for
12 papers · 1 filter
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…
Smoothed Online Combinatorial Optimization Using Imperfect Predictions
Kai Wang, Zhao Song, Georgios Theocharous +1
Smoothed online combinatorial optimization considers a learner who repeatedly chooses a combinatorial decision to minimize an unknown changing cost function with a penalty on switc…
Off-Policy Evaluation in Embedded Spaces
Jaron J. R. Lee, David Arbour, Georgios Theocharous
Off-policy evaluation methods are important in recommendation systems and search engines, where data collected under an existing logging policy is used to estimate the performance…
Edge-Compatible Reinforcement Learning for Recommendations
James E. Kostas, Philip S. Thomas, Georgios Theocharous
Most reinforcement learning (RL) recommendation systems designed for edge computing must either synchronize during recommendation selection or depend on an unprincipled patchwork c…
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…
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…
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…
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…
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…
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…
Learning Action Representations for Reinforcement Learning
Yash Chandak, Georgios Theocharous, James Kostas +2
Most model-free reinforcement learning methods leverage state representations (embeddings) for generalization, but either ignore structure in the space of actions or assume the str…
Posterior Sampling for Large Scale Reinforcement Learning
Georgios Theocharous, Zheng Wen, Yasin Abbasi-Yadkori +1
We propose a practical non-episodic PSRL algorithm that unlike recent state-of-the-art PSRL algorithms uses a deterministic, model-independent episode switching schedule. Our algor…