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Found 3 papers, 1 papers with code
When Your AIs Deceive You: Challenges with Partial Observability of Human Evaluators in Reward Learning
Past analyses of reinforcement learning from human feedback (RLHF) assume that the human fully observes the environment.
Development and Validation of a Deep Learning Algorithm for Improving Gleason Scoring of Prostate Cancer
For prostate cancer patients, the Gleason score is one of the most important prognostic factors, potentially determining treatment independent of the stage.
#Exploration: A Study of Count-Based Exploration for Deep Reinforcement Learning
In this work, we describe a surprising finding: a simple generalization of the classic count-based approach can reach near state-of-the-art performance on various high-dimensional and/or continuous deep RL benchmarks.
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