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RLHF-Blender: A Configurable Interactive Interface for Learning from Diverse Human Feedback
To use reinforcement learning from human feedback (RLHF) in practical applications, it is crucial to learn reward models from diverse sources of human feedback and to consider human factors involved in providing feedback of different types.
How to Enable Uncertainty Estimation in Proximal Policy Optimization
To overcome this, we formulate a Pareto optimization problem in which we simultaneously optimize for reward and OOD detection performance.
Out of Distribution (OOD) Detection
Reinforcement Learning (RL)
BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning
Robustness to adversarial perturbations has been explored in many areas of computer vision.
