Influencing Reinforcement Learning through Natural Language Guidance

TL;DR

This paper investigates how natural language advice can enhance reinforcement learning by replacing traditional human feedback with language-based guidance, improving policy shaping in complex environments.

Contribution

It introduces a novel approach that integrates natural language advice into reinforcement learning, replacing human feedback policies with language-generated guidance for better decision-making.

Findings

Natural language advice improves policy learning.

Language-guided reinforcement learning outperforms traditional methods.

The model effectively integrates advice to assist decision-making.

Abstract

Interactive reinforcement learning agents use human feedback or instruction to help them learn in complex environments. Often, this feedback comes in the form of a discrete signal that is either positive or negative. While informative, this information can be difficult to generalize on its own. In this work, we explore how natural language advice can be used to provide a richer feedback signal to a reinforcement learning agent by extending policy shaping, a well-known Interactive reinforcement learning technique. Usually policy shaping employs a human feedback policy to help an agent to learn more about how to achieve its goal. In our case, we replace this human feedback policy with policy generated based on natural language advice. We aim to inspect if the generated natural language reasoning provides support to a deep reinforcement learning agent to decide its actions successfully in any given environment. So, we design our model with three networks: first one is the experience driven, next is the advice generator and third one is the advice driven. While the experience driven reinforcement learning agent chooses its actions being influenced by the environmental reward, the advice driven neural network with generated feedback by the advice generator for any new state selects its actions to assist the reinforcement learning agent to better policy shaping.