RLInspect: An Interactive Visual Approach to Assess Reinforcement Learning Algorithm

TL;DR

RLInspect is an interactive visual tool designed to provide a comprehensive analysis of reinforcement learning models, helping users interpret training behavior beyond reward metrics and improve model robustness.

Contribution

The paper introduces RLInspect, a novel visual analytic tool that assesses multiple components of RL models for better interpretability and troubleshooting.

Findings

Enables detailed visualization of RL state, action, and architecture

Helps identify training issues not apparent from reward alone

Improves understanding and robustness of RL systems

Abstract

Reinforcement Learning (RL) is a rapidly growing area of machine learning that finds its application in a broad range of domains, from finance and healthcare to robotics and gaming. Compared to other machine learning techniques, RL agents learn from their own experiences using trial and error, and improve their performance over time. However, assessing RL models can be challenging, which makes it difficult to interpret their behaviour. While reward is a widely used metric to evaluate RL models, it may not always provide an accurate measure of training performance. In some cases, the reward may seem increasing while the model's performance is actually decreasing, leading to misleading conclusions about the effectiveness of the training. To overcome this limitation, we have developed RLInspect - an interactive visual analytic tool, that takes into account different components of the RL model - state, action, agent architecture and reward, and provides a more comprehensive view of the RL training. By using RLInspect, users can gain insights into the model's behaviour, identify issues during training, and potentially correct them effectively, leading to a more robust and reliable RL system.