Mediaboss Marketing

image:Key research areas in evolutionary reinforcement learning. view more

Credit: Hui Bai et al.

Evolutionary reinforcement learning is an exciting frontier in machine learning, combining the strengths of two distinct approaches: reinforcement learning and evolutionary computation. In evolutionary reinforcement learning, an intelligent agent learns optimal strategies by actively exploring different approaches and receiving rewards for successful performance. This innovative paradigm combines reinforcement learning's trial-and-error learning with evolutionary algorithms' ability to mimic natural selection, resulting in a powerful methodology for artificial intelligence development that promises breakthroughs in various domains.

A groundbreaking review article on evolutionary reinforcement learning was published Apr. 21 in Intelligent Computing, a Science Partner Journal. It sheds light on the latest advancements in the integration of evolutionary computation with reinforcement learning and presents a comprehensive survey of state-of-the-art methods.

Reinforcement learning, a subfield of machine learning, focuses on developing algorithms that learn to make decisions based on feedback from the environment. Remarkable examples of successful reinforcement learning include AlphaGo and, more recently, Google DeepMind robots that play soccer. However, reinforcement learning still faces several challenges, including the exploration and exploitation trade-off, reward design, generalization and credit assignment.

Evolutionary computation, which emulates the process of natural evolution to solve problems, offers a potential solution to the problems of reinforcement learning. By combining these two approaches, researchers created the field of evolutionary reinforcement learning.

Evolutionary reinforcement learning encompasses six key research areas:

Evolutionary reinforcement learning can solve complex reinforcement learning tasks, even in scenarios with rare or misleading rewards. However, it requires significant computational resources, making it computationally expensive. There is a growing need for more efficient methods, including improvements in encoding, sampling, search operators, algorithmic frameworks and evaluation.

While evolutionary reinforcement learning has shown promising results in addressing challenging reinforcement learning problems, further advancements are still possible. By enhancing its computational efficiency and exploring new benchmarks, platforms and applications, researchers in the field of evolutionary reinforcement learning can make evolutionary methods even more effective and useful for solving complex reinforcement learning tasks.

Intelligent Computing

Evolutionary Reinforcement Learning: A Survey

21-Apr-2023

Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Go here to see the original:
Evolutionary reinforcement learning promises further advances in ... - EurekAlert