mlsanityData to 7 Oct 2026

Ensemble Reinforcement Learning: A Survey

arXiv 2303.02618 ↗First posted 5 Mar 2023cs.LG cs.AI cs.NEcited by 16

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Abstract (arXiv)

Reinforcement Learning (RL) has emerged as a highly effective technique for addressing various scientific and applied problems. Despite its success, certain complex tasks remain challenging to be addressed solely with a single model and algorithm. In response, ensemble reinforcement learning (ERL), a promising approach that combines the benefits of both RL and ensemble learning (EL), has gained widespread popularity. ERL leverages multiple models or training algorithms to comprehensively explore the problem space and possesses strong generalization capabilities. In this study, we present a comprehensive survey on ERL to provide readers with an overview of recent advances and challenges in the field. Firstly, we provide an introduction to the background and motivation for ERL. Secondly, we conduct a detailed analysis of strategies such as model selection and combination that have been successfully implemented in ERL. Subsequently, we explore the application of ERL, summarize the datasets, and analyze the algorithms employed. Finally, we outline several open questions and discuss future research directions of ERL. By offering guidance for future scientific research and engineering applications, this survey significantly contributes to the advancement of ERL.

Key facts. Cited by 16 papers in the map (data through 7 Oct 2026). Builds on 43 of its 124 references that are in the map. First posted on arXiv 5 Mar 2023.

Builds on

43 of 124 references.

References · 43

  1. 2018Addressing Function Approximation Error in Actor-Critic Methodscited by 2,302
  2. 2016OpenAI Gymcited by 1,684
  3. 2015Prioritized Experience Replaycited by 1,070
  4. 2019Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reductioncited by 833
  5. 2016Deep Exploration via Bootstrapped DQNcited by 518
  6. 2019Model-Based Reinforcement Learning for Ataricited by 495
  7. 2017A Brief Survey of Deep Reinforcement Learningcited by 466
  8. 2021Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemblecited by 297
  9. 2021Ensemble deep learning: A reviewcited by 283
  10. 2021Randomized Ensembled Double Q-Learning: Learning Fast Without a Modelcited by 278
  11. 2018Selective Experience Replay for Lifelong Learningcited by 239
  12. 2021Offline-to-Online Reinforcement Learning via Balanced Replay and Pessimistic Q-Ensemblecited by 211
31 more
  1. 2018Sample-Efficient Reinforcement Learning with Stochastic Ensemble Value Expansioncited by 167
  2. 2020Maxmin Q-learning: Controlling the Estimation Bias of Q-learningcited by 117
  3. 2017Quality and Diversity Optimization: A Unifying Modular Frameworkcited by 111
  4. 2016Averaged-DQN: Variance Reduction and Stabilization for Deep Reinforcement Learningcited by 106
  5. 2021On the Importance of Hyperparameter Optimization for Model-based Reinforcement Learningcited by 59
  6. 2021Deep Reinforcement Learning for the Control of Robotic Manipulation: A Focussed Mini-Reviewcited by 34
  7. 2019Reinforcement Learning with Competitive Ensembles of Information-Constrained Primitivescited by 30
  8. 2021Ensemble Bootstrapping for Q-Learningcited by 30
  9. 2019Random Vector Functional Link Neural Network based Ensemble Deep Learningcited by 20
  10. 2022Towards Applicable Reinforcement Learning: Improving the Generalization and Sample Efficiency with Policy Ensemblecited by 18
  11. 2021Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitationcited by 17
  12. 2017Optimal and Scalable Caching for 5G Using Reinforcement Learning of Space-time Popularitiescited by 15
  13. 2021SENTINEL: Taming Uncertainty with Ensemble-based Distributional Reinforcement Learningcited by 15
  14. 2019Ensemble-Based Deep Reinforcement Learning for Chatbotscited by 11
  15. 2019Robust Opponent Modeling via Adversarial Ensemble Reinforcement Learning in Asymmetric Imperfect-Information Gamescited by 11
  16. 2020SEERL: Sample Efficient Ensemble Reinforcement Learningcited by 11
  17. 2021ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement Learningcited by 9
  18. 2018Effective Exploration for Deep Reinforcement Learning via Bootstrapped Q-Ensembles under Tsallis Entropy Regularizationcited by 8
  19. 2022DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learningcited by 8
  20. 2020Reinforcement Learning based dynamic weighing of Ensemble Models for Time Series Forecastingcited by 6
  21. 2020Distributed Resource Scheduling for Large-Scale MEC Systems: A Multi-Agent Ensemble Deep Reinforcement Learning with Imitation Accelerationcited by 5
  22. 2021MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learningcited by 5
  23. 2022POLTER: Policy Trajectory Ensemble Regularization for Unsupervised Reinforcement Learningcited by 3
  24. 2018Bayesian Inference with Anchored Ensembles of Neural Networks, and Application to Exploration in Reinforcement Learningcited by 2
  25. 2018Interpretable Reinforcement Learning with Ensemble Methodscited by 2
  26. 2021Federated Ensemble Model-based Reinforcement Learning in Edge Computingcited by 2
  27. 2022DEFT: Diverse Ensembles for Fast Transfer in Reinforcement Learningcited by 2
  28. 2023Accelerating Policy Gradient by Estimating Value Function from Prior Computation in Deep Reinforcement Learningcited by 2
  29. 2021Ensemble Consensus-based Representation Deep Reinforcement Learning for Hybrid FSO/RF Communication Systemscited by 1
  30. 2022Random Ensemble Reinforcement Learning for Traffic Signal Controlcited by 1
  31. 2022An Optimization Method-Assisted Ensemble Deep Reinforcement Learning Algorithm to Solve Unit Commitment Problemscited by 1

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 3; 2024: 8; 2025: 4; 2026: 1.

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  1. 2023Structure in Deep Reinforcement Learning: A Survey and Open Problemscited by 16
  2. 2023Reinforcement Learning-assisted Evolutionary Algorithm: A Survey and Research Opportunitiescited by 8
  3. 2023Graph-enabled Reinforcement Learning for Time Series Forecasting with Adaptive Intelligencecited by 5
  4. 2024Mitigating Suboptimality of Deterministic Policy Gradients in Complex Q-functionscited by 3
  5. 2024Fusion-PSRO: Nash Policy Fusion for Policy Space Response Oraclescited by 2
  6. 2025Dynamic Optimizations of LLM Ensembles with Two-Stage Reinforcement Learning Agentscited by 2
  7. 2024Dataset Clustering for Improved Offline Policy Learningcited by 1
  8. 2024vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvementcited by 1
  9. 2024Oracle-Efficient Reinforcement Learning for Max Value Ensemblescited by 1
  10. 2024Prediction Instability in Machine Learning Ensemblescited by 1
  11. 2024Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learningcited by 1
  12. 2025Multi-Objective Reinforcement Learning for Large Language Model Optimization: Visionary Perspectivecited by 1
4 more
  1. 2024Partial Models for Building Adaptive Model-Based Reinforcement Learning Agentscited by 0
  2. 2025Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme Onecited by 0
  3. 2025Decision Flow Policy Optimizationcited by 0
  4. 2026Quantum-Inspired Reinforcement Learning for Secure and Sustainable AIoT-Driven Supply Chain Systemscited by 0