The paper explores a novel approach to training deep reinforcement learning (RL) systems using human preferences instead of predefined reward functions. It aims to bridge the gap between subjective, complex goals and the traditional RL methods that rely on mathematical reward functions.

The paper introduces a method that significantly reduces the need for human oversight in training deep RL agents, allowing them to learn complex behaviors with minimal human input. This approach has shown promising results in both simulated robotics and Atari games, achieving human-level performance with a fraction of the human effort required by traditional RL methods.

Read full paper: https://arxiv.org/abs/1706.03741

Tags: Reinforcement Learning, Deep Learning, AI Safety