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Evolutionary Reinforcement Learning: A Systematic Review and Future Directions

Yuanguo Lin, Fan Lin, Guorong Cai, Hong Chen, Lixin Zou, Pengcheng Wu

TL;DR

Evolutionary Reinforcement Learning (EvoRL) merges evolutionary algorithms with reinforcement learning to address parameter sensitivity, sample efficiency, and scalability challenges in RL while leveraging the global search and diversity advantages of EAs. The paper provides a structured survey across EvoRL methods, categorizing them by the underlying EA (e.g., Evolutionary Strategy, Genetic Algorithm, Cross-Entropy Method, Population-Based Training) and detailing representative algorithms and their empirical benefits in policy search, value estimation, and multi-agent contexts. It highlights open issues—scalability to high-dimensional spaces, adaptability to dynamic environments, sample/data efficiency, adversarial robustness, and ethics/fairness—and outlines future directions such as meta-evolutionary strategies, self-adaptation, transfer learning, heterogeneous networks, interpretability, and integration with large language models. Overall, the work offers a comprehensive taxonomy and practical guidance for advancing EvoRL in complex, evolving AI challenges.

Abstract

In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution. EvoRL integrates EAs and reinforcement learning, presenting a promising avenue for training intelligent agents. This systematic review firstly navigates through the technological background of EvoRL, examining the symbiotic relationship between EAs and reinforcement learning algorithms. We then delve into the challenges faced by both EAs and reinforcement learning, exploring their interplay and impact on the efficacy of EvoRL. Furthermore, the review underscores the need for addressing open issues related to scalability, adaptability, sample efficiency, adversarial robustness, ethic and fairness within the current landscape of EvoRL. Finally, we propose future directions for EvoRL, emphasizing research avenues that strive to enhance self-adaptation and self-improvement, generalization, interpretability, explainability, and so on. Serving as a comprehensive resource for researchers and practitioners, this systematic review provides insights into the current state of EvoRL and offers a guide for advancing its capabilities in the ever-evolving landscape of artificial intelligence.

Evolutionary Reinforcement Learning: A Systematic Review and Future Directions

TL;DR

Evolutionary Reinforcement Learning (EvoRL) merges evolutionary algorithms with reinforcement learning to address parameter sensitivity, sample efficiency, and scalability challenges in RL while leveraging the global search and diversity advantages of EAs. The paper provides a structured survey across EvoRL methods, categorizing them by the underlying EA (e.g., Evolutionary Strategy, Genetic Algorithm, Cross-Entropy Method, Population-Based Training) and detailing representative algorithms and their empirical benefits in policy search, value estimation, and multi-agent contexts. It highlights open issues—scalability to high-dimensional spaces, adaptability to dynamic environments, sample/data efficiency, adversarial robustness, and ethics/fairness—and outlines future directions such as meta-evolutionary strategies, self-adaptation, transfer learning, heterogeneous networks, interpretability, and integration with large language models. Overall, the work offers a comprehensive taxonomy and practical guidance for advancing EvoRL in complex, evolving AI challenges.

Abstract

In response to the limitations of reinforcement learning and evolutionary algorithms (EAs) in complex problem-solving, Evolutionary Reinforcement Learning (EvoRL) has emerged as a synergistic solution. EvoRL integrates EAs and reinforcement learning, presenting a promising avenue for training intelligent agents. This systematic review firstly navigates through the technological background of EvoRL, examining the symbiotic relationship between EAs and reinforcement learning algorithms. We then delve into the challenges faced by both EAs and reinforcement learning, exploring their interplay and impact on the efficacy of EvoRL. Furthermore, the review underscores the need for addressing open issues related to scalability, adaptability, sample efficiency, adversarial robustness, ethic and fairness within the current landscape of EvoRL. Finally, we propose future directions for EvoRL, emphasizing research avenues that strive to enhance self-adaptation and self-improvement, generalization, interpretability, explainability, and so on. Serving as a comprehensive resource for researchers and practitioners, this systematic review provides insights into the current state of EvoRL and offers a guide for advancing its capabilities in the ever-evolving landscape of artificial intelligence.
Paper Structure (38 sections, 25 equations, 2 figures, 3 tables)

This paper contains 38 sections, 25 equations, 2 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Evolutionary Algorithms
  4. Reinforcement Learning
  5. Methods of Evolutionary Reinforcement Learning
  6. Evolutionary Strategy
  7. Genetic Algorithm
  8. Cross-Entropy Method
  9. Population-Based Training
  10. Other EAs
  11. Challenges in Evolutionary Reinforcement Learning
  12. Issues Encountered by Reinforcement Learning
  13. Parameters Sensitivity
  14. Sparse Reward
  15. Local Optima
  16. ...and 23 more sections

Figures (2)

  • Figure 1: A General Workflow of ES-based EvoRL. The core evolutionary operation here is the mutation, which enhances the diversity of the population.
  • Figure 2: A General Workflow of GA-based EvoRL. The EvoRL retains good genes through crossover to produce better individuals.