# Dex: Incremental Learning for Complex Environments in Deep Reinforcement Learning

## TL;DR

This paper presents Dex, a toolkit for reinforcement learning environments, introduces incremental learning as a novel method for continual learning, and demonstrates its effectiveness across multiple environments with qualitative analysis.

## Contribution

The paper introduces Dex, a new toolkit for reinforcement learning, and proposes incremental learning as a novel approach for continual learning in complex environments.

## Key findings

- Incremental learning outperforms standard methods in Dex environments.
- A saliency method reveals how incremental learning affects network attention.
- Dex provides a versatile platform for evaluating reinforcement learning and continual learning methods.

## Abstract

This paper introduces Dex, a reinforcement learning environment toolkit specialized for training and evaluation of continual learning methods as well as general reinforcement learning problems. We also present the novel continual learning method of incremental learning, where a challenging environment is solved using optimal weight initialization learned from first solving a similar easier environment. We show that incremental learning can produce vastly superior results than standard methods by providing a strong baseline method across ten Dex environments. We finally develop a saliency method for qualitative analysis of reinforcement learning, which shows the impact incremental learning has on network attention.