mlsanityData to 14 Oct 2026

Evolutionary Reinforcement Learning via Cooperative Coevolution

arXiv 2404.14763 ↗First posted 23 Apr 2024cs.NE cs.AIcited by 1

On the map: Deep reinforcement learning methods → RL for games and combinatorial optimization

Abstract (arXiv)

Recently, evolutionary reinforcement learning has obtained much attention in various domains. Maintaining a population of actors, evolutionary reinforcement learning utilises the collected experiences to improve the behaviour policy through efficient exploration. However, the poor scalability of genetic operators limits the efficiency of optimising high-dimensional neural networks.To address this issue, this paper proposes a novel cooperative coevolutionary reinforcement learning (CoERL) algorithm. Inspired by cooperative coevolution, CoERL periodically and adaptively decomposes the policy optimisation problem into multiple subproblems and evolves a population of neural networks for each of the subproblems. Instead of using genetic operators, CoERL directly searches for partial gradients to update the policy. Updating policy with partial gradients maintains consistency between the behaviour spaces of parents and offspring across generations.The experiences collected by the population are then used to improve the entire policy, which enhances the sampling efficiency.Experiments on six benchmark locomotion tasks demonstrate that CoERL outperforms seven state-of-the-art algorithms and baselines.Ablation study verifies the unique contribution of CoERL's core ingredients.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods. Builds on 20 of its 43 references that are in the map. First posted on arXiv 23 Apr 2024.

Builds on

20 of 43 references.

References · 20

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,885
  3. 2017Evolution Strategies as a Scalable Alternative to Reinforcement Learningcited by 574
  4. 2017Noisy Networks for Explorationcited by 266
  5. 2021Tianshou: a Highly Modularized Deep Reinforcement Learning Librarycited by 143
  6. 2018Evolution-Guided Policy Gradient in Reinforcement Learningcited by 83
  7. 2018CEM-RL: Combining evolutionary and gradient-based methods for policy searchcited by 58
  8. 2019Collaborative Evolutionary Reinforcement Learningcited by 37
  9. 2020Neuroevolution in Deep Neural Networks: Current Trends and Future Challengescited by 33
  10. 2023Evolutionary Reinforcement Learning: A Surveycited by 29
  11. 2019Proximal Distilled Evolutionary Reinforcement Learningcited by 27
  12. 2017ES Is More Than Just a Traditional Finite-Difference Approximatorcited by 24
8 more
  1. 2022Combining Evolution and Deep Reinforcement Learning for Policy Search: a Surveycited by 23
  2. 2017Safe Mutations for Deep and Recurrent Neural Networks through Output Gradientscited by 15
  3. 2023Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancercited by 14
  4. 2022A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learningcited by 13
  5. 2020Evolutionary Reinforcement Learning via Cooperative Coevolutionary Negatively Correlated Searchcited by 8
  6. 2016High-dimensional Black-box Optimization via Divide and Approximate Conquercited by 4
  7. 2017Policy Optimization by Genetic Distillationcited by 3
  8. 2015Algorithm Portfolios for Noisy Optimizationcited by 1

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Cooperative Coevolution for Resource-Constrained Agentic LLM Post-Trainingcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024Ancestral Reinforcement Learning: Unifying Zeroth-Order Optimization and Genetic Algorithms for Reinforcement Learning
  2. 2025Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation
  3. 2025Evolutionary Policy Optimization
  4. 2025Wisdom of the Crowd: Reinforcement Learning from Coevolutionary Collective Feedback
  5. 2024Auto-configuring Exploration-Exploitation Tradeoff in Evolutionary Computation via Deep Reinforcement Learning
  6. 2026Continual Reinforcement Learning with Neuroevolution
  7. 2024Deep Reinforcement Learning for Dynamic Algorithm Selection: A Proof-of-Principle Study on Differential Evolution
  8. 2024Evolution of Societies via Reinforcement Learning