Reducing Reward Dependence in RL Through Adaptive Confidence Discounting

TL;DR

This paper introduces a reinforcement learning method that minimizes reliance on costly or human feedback by requesting rewards only when the model's confidence is low, thus improving learning efficiency.

Contribution

The authors propose a novel adaptive confidence-based reward discounting algorithm that reduces reward dependence while maintaining policy quality.

Findings

Achieves comparable performance with only 20% of the rewards used by baseline.

Reduces reward dependence without sacrificing learning effectiveness.

Effective in environments with expensive or human-in-the-loop reward signals.

Abstract

In human-in-the-loop reinforcement learning or environments where calculating a reward is expensive, the costly rewards can make learning efficiency challenging to achieve. The cost of obtaining feedback from humans or calculating expensive rewards means algorithms receiving feedback at every step of long training sessions may be infeasible, which may limit agents' abilities to efficiently improve performance. Our aim is to reduce the reliance of learning agents on humans or expensive rewards, improving the efficiency of learning while maintaining the quality of the learned policy. We offer a novel reinforcement learning algorithm that requests a reward only when its knowledge of the value of actions in an environment state is low. Our approach uses a reward function model as a proxy for human-delivered or expensive rewards when confidence is high, and asks for those explicit rewards only when there is low confidence in the model's predicted rewards and/or action selection. By reducing dependence on the expensive-to-obtain rewards, we are able to learn efficiently in settings where the logistics or expense of obtaining rewards may otherwise prohibit it. In our experiments our approach obtains comparable performance to a baseline in terms of return and number of episodes required to learn, but achieves that performance with as few as 20% of the rewards.