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06/26/2023
Supervised Pretraining Can Learn In-Context Reinforcement Learning
Large transformer models trained on diverse datasets have shown a remark...
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05/16/2023
Coagent Networks: Generalized and Scaled
Coagent networks for reinforcement learning (RL) [Thomas and Barto, 2011...
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05/01/2023
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition
Representation learning and exploration are among the key challenges for...
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02/23/2023
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments
In this work, we consider the off-policy policy evaluation problem for c...
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02/06/2023
Optimization using Parallel Gradient Evaluations on Multiple Parameters
We propose a first-order method for convex optimization, where instead o...
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01/24/2023
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments
Methods for sequential decision-making are often built upon a foundation...
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12/06/2022
Understanding Self-Predictive Learning for Reinforcement Learning
We study the learning dynamics of self-predictive learning for reinforce...
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12/16/2021
On Optimizing Interventions in Shared Autonomy
Shared autonomy refers to approaches for enabling an autonomous agent to...
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11/06/2021
SOPE: Spectrum of Off-Policy Estimators
Many sequential decision making problems are high-stakes and require off...
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04/26/2021
Universal Off-Policy Evaluation
When faced with sequential decision-making problems, it is often useful ...
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01/25/2021
High-Confidence Off-Policy (or Counterfactual) Variance Estimation
Many sequential decision-making systems leverage data collected using pr...
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10/23/2020
Towards Safe Policy Improvement for Non-Stationary MDPs
Many real-world sequential decision-making problems involve critical sys...
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09/15/2020
Reinforcement Learning for Strategic Recommendations
Strategic recommendations (SR) refer to the problem where an intelligent...
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06/30/2020
Evaluating the Performance of Reinforcement Learning Algorithms
Performance evaluations are critical for quantifying algorithmic advance...
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05/17/2020
Optimizing for the Future in Non-Stationary MDPs
Most reinforcement learning methods are based upon the key assumption th...
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06/06/2019
Classical Policy Gradient: Preserving Bellman's Principle of Optimality
We propose a new objective function for finite-horizon episodic Markov d...
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06/05/2019
Reinforcement Learning When All Actions are Not Always Available
The Markov decision process (MDP) formulation used to model many real-wo...
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06/05/2019
Lifelong Learning with a Changing Action Set
In many real-world sequential decision making problems, the number of av...
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02/01/2019
Learning Action Representations for Reinforcement Learning
Most model-free reinforcement learning methods leverage state representa...
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05/31/2018
Fusion Graph Convolutional Networks
Semi-supervised node classification involves learning to classify unlabe...
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05/31/2018
