Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning

• URL: http://arxiv.org/abs/2405.00746v1
• Date: Tue, 30 Apr 2024 18:58:33 GMT
• Title: Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning
• Authors: Calarina Muslimani, Matthew E. Taylor,
• Abstract summary: Sub-optimal Data Pre-training, SDP, is an approach that leverages reward-free, sub-optimal data to improve HitL RL algorithms. We show SDP can significantly improve or achieve competitive performance with state-of-the-art HitL RL algorithms.
• Score: 7.07264650720021
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: To create useful reinforcement learning (RL) agents, step zero is to design a suitable reward function that captures the nuances of the task. However, reward engineering can be a difficult and time-consuming process. Instead, human-in-the-loop (HitL) RL allows agents to learn reward functions from human feedback. Despite recent successes, many of the HitL RL methods still require numerous human interactions to learn successful reward functions. To improve the feedback efficiency of HitL RL methods (i.e., require less feedback), this paper introduces Sub-optimal Data Pre-training, SDP, an approach that leverages reward-free, sub-optimal data to improve scalar- and preference-based HitL RL algorithms. In SDP, we start by pseudo-labeling all low-quality data with rewards of zero. Through this process, we obtain free reward labels to pre-train our reward model. This pre-training phase provides the reward model a head start in learning, whereby it can identify that low-quality transitions should have a low reward, all without any actual feedback. Through extensive experiments with a simulated teacher, we demonstrate that SDP can significantly improve or achieve competitive performance with state-of-the-art (SOTA) HitL RL algorithms across nine robotic manipulation and locomotion tasks.

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