# Proximal Distilled Evolutionary Reinforcement Learning

## TL;DR

This paper introduces PDERL, a novel evolutionary reinforcement learning method that uses learning-based variation operators to improve scalability and performance in complex robotic tasks, outperforming existing algorithms.

## Contribution

The paper proposes PDERL, a hierarchical integration of evolution and learning with novel variation operators that enhance scalability for DNNs in RL tasks.

## Key findings

- PDERL outperforms ERL, PPO, and TD3 in robot locomotion tasks.
- Learning-based variation operators mitigate catastrophic forgetting.
- Hierarchical integration improves scalability and performance.

## Abstract

Reinforcement Learning (RL) has achieved impressive performance in many complex environments due to the integration with Deep Neural Networks (DNNs). At the same time, Genetic Algorithms (GAs), often seen as a competing approach to RL, had limited success in scaling up to the DNNs required to solve challenging tasks. Contrary to this dichotomic view, in the physical world, evolution and learning are complementary processes that continuously interact. The recently proposed Evolutionary Reinforcement Learning (ERL) framework has demonstrated mutual benefits to performance when combining the two methods. However, ERL has not fully addressed the scalability problem of GAs. In this paper, we show that this problem is rooted in an unfortunate combination of a simple genetic encoding for DNNs and the use of traditional biologically-inspired variation operators. When applied to these encodings, the standard operators are destructive and cause catastrophic forgetting of the traits the networks acquired. We propose a novel algorithm called Proximal Distilled Evolutionary Reinforcement Learning (PDERL) that is characterised by a hierarchical integration between evolution and learning. The main innovation of PDERL is the use of learning-based variation operators that compensate for the simplicity of the genetic representation. Unlike traditional operators, our proposals meet the functional requirements of variation operators when applied on directly-encoded DNNs. We evaluate PDERL in five robot locomotion settings from the OpenAI gym. Our method outperforms ERL, as well as two state-of-the-art RL algorithms, PPO and TD3, in all tested environments.