Broad-persistent Advice for Interactive Reinforcement Learning Scenarios

TL;DR

This paper introduces a method for retaining and reusing advice in interactive reinforcement learning, leading to faster learning and improved performance by leveraging broad, persistent guidance.

Contribution

It proposes a novel approach to retain and reuse advice, extending beyond real-time interactions to enhance learning efficiency in reinforcement learning agents.

Findings

Broad-persistent advice improves agent performance

Reduces number of interactions needed for training

Enhances generalization of advice across states

Abstract

The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents. Current interactive reinforcement learning research has been limited to real-time interactions that offer relevant user advice to the current state only. Moreover, the information provided by each interaction is not retained and instead discarded by the agent after a single use. In this paper, we present a method for retaining and reusing provided knowledge, allowing trainers to give general advice relevant to more than just the current state. Results obtained show that the use of broad-persistent advice substantially improves the performance of the agent while reducing the number of interactions required for the trainer.