18 citations · 21 across the 6 of their papers we have counts for
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cs.AI2023
Methods and Mechanisms for Interactive Novelty Handling in Adversarial Environments
Tung Thai, Ming Shen, Mayank Garg +10
Learning to detect, characterize and accommodate novelties is a challenge that agents operating in open-world domains need to address to be able to guarantee satisfactory task perf…
cs.AI2023★ 1 cited
A State Augmentation based approach to Reinforcement Learning from Human Preferences
Mudit Verma, Subbarao Kambhampati
Reinforcement Learning has suffered from poor reward specification, and issues for reward hacking even in simple enough domains. Preference Based Reinforcement Learning attempts to…