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02/27/2023
Systematic Rectification of Language Models via Dead-end Analysis
With adversarial or otherwise normal prompts, existing large language mo...
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03/17/2022
Semi-Markov Offline Reinforcement Learning for Healthcare
Reinforcement learning (RL) tasks are typically framed as Markov Decisio...
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03/14/2022
Orchestrated Value Mapping for Reinforcement Learning
We present a general convergent class of reinforcement learning algorith...
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10/08/2021
Medical Dead-ends and Learning to Identify High-risk States and Treatments
Machine learning has successfully framed many sequential decision making...
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07/13/2021
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks
We propose the k-Shortest-Path (k-SP) constraint: a novel constraint on ...
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11/23/2020
An Empirical Study of Representation Learning for Reinforcement Learning in Healthcare
Reinforcement Learning (RL) has recently been applied to sequential esti...
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10/28/2020
Learning to Represent Action Values as a Hypergraph on the Action Vertices
Action-value estimation is a critical component of many reinforcement le...
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06/03/2019
Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning
In an effort to better understand the different ways in which the discou...
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04/03/2017
Multi-Advisor Reinforcement Learning
We consider tackling a single-agent RL problem by distributing it to n l...
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12/15/2016
Separation of Concerns in Reinforcement Learning
In this paper, we propose a framework for solving a single-agent task by...
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06/10/2016