Towards Governing Agent's Efficacy: Action-Conditional $\beta$-VAE for Deep Transparent Reinforcement Learning

TL;DR

This paper introduces an action-conditional $eta$-VAE to improve interpretability and controllability of deep reinforcement learning agents by learning disentangled, interpretable latent features that reflect the agent's influence on its environment.

Contribution

It proposes a novel method combining disentangled representation learning with reinforcement learning to enhance transparency and enable governance of agent behaviors.

Findings

Latent features learned are interpretable and action-conditioned.

The method models the distribution of visited states effectively.

It facilitates ex post facto governance of RL agent behaviors.

Abstract

We tackle the blackbox issue of deep neural networks in the settings of reinforcement learning (RL) where neural agents learn towards maximizing reward gains in an uncontrollable way. Such learning approach is risky when the interacting environment includes an expanse of state space because it is then almost impossible to foresee all unwanted outcomes and penalize them with negative rewards beforehand. Unlike reverse analysis of learned neural features from previous works, our proposed method \nj{tackles the blackbox issue by encouraging} an RL policy network to learn interpretable latent features through an implementation of a disentangled representation learning method. Toward this end, our method allows an RL agent to understand self-efficacy by distinguishing its influences from uncontrollable environmental factors, which closely resembles the way humans understand their scenes. Our experimental results show that the learned latent factors not only are interpretable, but also enable modeling the distribution of entire visited state space with a specific action condition. We have experimented that this characteristic of the proposed structure can lead to ex post facto governance for desired behaviors of RL agents.