Important
Official companion repository for Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms.
This repository helps readers enter the ERL field faster, reproduce representative methods, navigate the survey taxonomy, and track new papers and code. It currently indexes 270 canonical algorithm works and curates 86 independently reviewed Chinese and English guide mappings across 80 unique URLs, covering 54 works.
The research companion for studying, reproducing, and extending ERL
Evolutionary Reinforcement Learning (ERL) combines the global search and population diversity of evolutionary algorithms with the learning efficiency and decision-making capabilities of reinforcement learning. Alongside papers and verified code, this collection provides independently reviewed 中文 / English explanations so readers can move quickly from the survey taxonomy to understanding, reproducing, and extending representative methods.
Read the survey · Browse the taxonomy · 中文 / English reading guides · Start from code · See recent work
| 3 major directions | 21 maintained branches | 270 canonical algorithm works | 80 with verified public code |
|---|---|---|---|
| EA → RL · RL → EA · Synergy | Survey branches plus maintained extensions | 264 taxonomy works + 6 recent works | Canonical works with an exact code match |
Reproducible count policy (30 Aug 2026): one canonical work equals one unique paper record, regardless of aliases, variants, or cross-listings. The 270 total excludes tooling/benchmarks, field overviews, and the independent watchlist. Code coverage counts canonical works with an exact author/official or explicitly labeled community implementation.
Important
Which survey version should you use? Cite the IEEE version of record, read arXiv v5 for the latest paper content, and use this repository for code and ongoing updates. See the exact differences ↓
| 01 · EA assists RL | 02 · RL assists EA | 03 · EA and RL collaborate |
|---|---|---|
| Evolution searches policies, actions, hyperparameters, curricula, or reward structures for RL. | RL configures initialization, evaluation, variation, operator selection, and EA hyperparameters. | Population search and gradient learning exchange information or optimize jointly. |
| Explore EA-assisted RL ↓ | Explore RL-assisted EA ↓ | Explore synergistic ERL ↓ |
Need reproducible baselines? Start from the code-first list and frameworks.
Need an explanation first? Start from the integrated bilingual reading-guide overview, then open the full work-by-work guide when you need a specific method.
Tracking the newest work? Go to recent and emerging directions.
Note
Code labels: repository links are author-maintained unless explicitly marked as a community implementation. Not found means no verified public implementation was located. Some methods are intentionally cross-listed when one mechanism contributes to multiple branches.
Changelog
- 30 Aug 2026 — completed a multi-scope coverage adjudication: added 120 canonical works, expanded the collection to 270 papers and 80 verified code artifacts, corrected QD-PG metadata, and retained 14 independently tracked boundary items in a non-counting watchlist.
- 29 Aug 2026 — refined the bilingual reading guide to 86 verified work-link mappings across 54 works after a stricter mechanism-depth audit removed mismatched and installation-only references.
- 28 Aug 2026 — synchronized the collection with arXiv v5; added Reward Design, Dynamic Operator Selection, missing QD methods, recent 2024–2026 work, verified code repositories, frameworks, and updated taxonomy figures.
- 26 Jul 2024 — the survey was accepted by IEEE Transactions on Evolutionary Computation.
Full section index
The reading guide is integrated into this repository as a second layer over the survey taxonomy: use the compact paths below to enter a direction quickly, then open the complete work-by-work guide for all reviewed resources.
| 270 works reviewed | 54 works covered | 27 with Chinese material | 45 with English material | 86 verified mappings |
|---|---|---|---|---|
| Full collection | At least one guide | 中文讲解 | English guides | 80 unique URLs |
Core marks substantial, work-specific explanations. Extended marks concise but mechanism-bearing overview sections, project documentation, thesis chapters, posters, or talk slides. Installation-only repositories, abstract restatements, and references that merely mention a method are excluded.
Guide coverage and mapping counts are deduplicated by canonical paper/work URL, language, guide URL, and Core/Extended label; aliases and cross-lists do not add mappings.
| Research direction | Coverage | Suggested starting points |
|---|---|---|
| EA assists RL | 13 / 47 works | PBT · English · Go-Explore · 中文 · Qt-Opt · English |
| RL assists EA | 15 / 105 works | DeepACO · 中文 · LOOP · English · TD-MPC · 中文 |
| EA and RL collaborate | 23 / 112 works | RACE · 中文 · Eureka · English · ROSKA · 中文 |
| Recent directions | 3 / 6 works | Follow verified projects and author material in the full guide. |
Browse the complete bilingual guide →
Survey v5 organizes ERL into three major directions and sixteen research branches. The maintained repository uses twenty-one branches after adding five evidence-backed extensions: algorithm/update-rule discovery, quality-diversity, dynamic resource allocation, adaptive/staged/stabilized coupling, and representation/search decomposition.
Figure 1. Taxonomy of evolutionary reinforcement learning in survey v5.
Note
Survey v5 Table V and the body text additionally include LERO under Reward Design and discuss XCSRG alongside XCSG. The detailed tables below follow the more complete body and tabular taxonomy.
Figure 2. Four integration patterns connecting EA and RL across solution and decomposed problem spaces.
| Version | Status and recommended use |
|---|---|
| Official published version | IEEE TEVC, Vol. 29, No. 5, pp. 1707–1728, 2025 · DOI The peer-reviewed version of record. Use it for formal citation. |
| Current paper version | arXiv v5, revised 24 May 2026 · PDF The author-updated manuscript with expanded method coverage. Use it for the latest survey content. |
| Current repository version | Synced 30 Aug 2026 with v5, multi-scope coverage adjudication, verified code links, frameworks, and a separately counted watchlist. Use it for implementation and tracking. |
Difference at a glance: IEEE = archival citation · arXiv v5 = latest paper content · GitHub = maintained implementation index
The repository includes code availability, tooling, and newer work that should not be treated as part of the archival IEEE article.
Paper: Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms — Pengyi Li, Jianye Hao, Hongyao Tang, Xian Fu, Yan Zheng, and Ke Tang.
The following papers have public implementations verified against the paper, project page, or authors' repositories and are useful starting points. Community reimplementations are kept out of this short list and labeled explicitly in the full taxonomy.
Verified code-first collection (31 entries)
EA → RL · Evolutionary search supports reinforcement learning while RL remains responsible for solving the task.
↑ Overview · Next: RL-assisted EA →
| Method | Paper | Venue | Code | Guide |
|---|---|---|---|---|
| EQ | Reinforcement Learning Beyond the Bellman Equation: Exploring Critic Objectives Using Evolution | ALIFE 2020 | ajleite/RLBeyondBellman | Not found |
| Supe-RL | Genetic Soft Updates for Policy Evolution in Deep Reinforcement Learning | ICLR 2021 | Not found | EN · Core 中文 · Extended |
| VFS | Improving Deep Policy Gradients with Value Function Search | ICLR 2023 | Not found | Not found |
Browse all 11 methods
| Method | Paper | Venue | Code | Guide |
|---|---|---|---|---|
| DQNClipped / DQNReg | Evolving Reinforcement Learning Algorithms | ICLR 2021 | google/brain_autorl | EN 1 · Core EN 2 · Core |
| LPO | Discovered Policy Optimisation | NeurIPS 2022 | luchris429/discovered-policy-optimisation | 中文 · Extended |
| TA-LPG / TA-LPO | Discovering Temporally-Aware RL Algorithms | ICLR 2024 | EmptyJackson/groove | Not found |
| LLM RL Algorithm Discovery | Evolutionary Discovery of Reinforcement Learning Algorithms via Large Language Models | GECCO 2026, pp. 348–355 | Not found | Not found |
| LLM Multiagent Algorithm Discovery | Discovering Multiagent Learning Algorithms with Large Language Models | AAMAS 2026 | Not found | Not found |
| EPG | Evolved Policy Gradients | NeurIPS 2018 | openai/EPG | Not found |
Browse all 18 methods
RL → EA · Reinforcement learning configures or improves one or more stages of an evolutionary algorithm.
← EA-assisted RL · ↑ Overview · Next: Synergistic ERL →
| Method | Paper | Venue | Code | Guide |
|---|---|---|---|---|
| NGGP | Symbolic Regression via Neural-Guided Genetic Programming Population Seeding | NeurIPS 2021 | dso-org/deep-symbolic-optimization | EN · Extended |
| RL-guided GA | Rule-Based Reinforcement Learning Methodology to Inform Evolutionary Algorithms for Constrained Optimization of Engineering Applications | Knowledge-Based Systems 2021 | aims-umich/neorl | Not found |
| DeepACO | DeepACO: Neural-Enhanced Ant Systems for Combinatorial Optimization | NeurIPS 2023 | henry-yeh/DeepACO | 中文 1 · Core 中文 2 · Core EN · Extended |
This branch contains seven primary RL-assisted evaluation works plus three secondary mechanism cross-lists. SC, PGPS, and ERL-Re2 remain counted only under their synergistic canonical branches.
Browse all 8 canonical works
| Method | Paper | Venue | Code | Guide |
|---|---|---|---|---|
| GPO | Policy Optimization by Genetic Distillation | — | Not found | EN · Extended |
| CEM-RL | Combining Evolutionary and Gradient-Based Methods for Policy Search | ICLR 2019 | apourchot/CEM-RL | 中文 · Extended EN · Extended |
| CEM-ACER | Guiding Evolutionary Strategies with Off-Policy Actor-Critic | — | Not found | Not found |
| PBRL | Population Based Reinforcement Learning | — | Not found | Not found |
| DEPRL | Diversity Evolutionary Policy Deep Reinforcement Learning | Complexity 2021 | Not found | Not found |
| Wuji | Wuji: Automatic Online Combat Game Testing Using Evolutionary Deep Reinforcement Learning | ASE 2019 | NeteaseFuxiRL/wuji | 中文 · Core |
| PE-DRL | Stable and Sample-Efficient Policy Search for Continuous Control via Hybridizing Phenotypic Evolutionary Algorithm with the Double Actors Regularized Critics | GECCO 2023 | Not found | Not found |
| EDA-PG | Evolutionary Deep Reinforcement Learning via Hybridizing Estimation-of-Distribution Algorithms with Policy Gradients | IEEE CEC 2024 | Not found | Not found |
Browse all 15 canonical works
This branch was empty in the previous README. Survey v5 identifies sixteen methods.
Browse all 30 methods
This maintained branch extends the survey's hyperparameter-configuration grouping to cover learned, state-dependent algorithm configuration. Survey v5 contains AGA, LTO, RL-DAC, REM, Q-LSHADE & DQ-HSES, MADAC, qlDE, and RLDE; GS-DAC is the round-2 addition.
Browse all 29 canonical works
| Method | Paper | Venue | Code | Guide |
|---|---|---|---|---|
| RLDE-ARA | Reinforcement learning assisted differential evolution with adaptive resource allocation strategy for multimodal optimization problems | Swarm and Evolutionary Computation 2025 | Not found | Not found |
| DRL Multi-Restart CMA-ES | Deep Reinforcement Learning for Multi-Restart Metaheuristics: An Environment Design for a Hybrid of Unbiased Exploratory Search and Covariance Matrix Adaptation Evolution Strategy | International Transactions in Operational Research 2026 | Not found | Not found |
Browse all 11 canonical works
EA and RL both contribute directly to solving the task, either in a shared solution space or through decomposed subproblems.
Browse all 35 canonical works
EvoRainbow and its experimental label refer to one paper and count once. CORE remains primarily single-agent ERL; solution-space/policy-space co-optimization is retained as a secondary tag, and its recent-work row is a non-counting cross-list.
Reward search is split by mechanism so classical evolutionary reward search is not conflated with LLM-generated reward programs. Evo-Reward is counted here; EA-assisted reward search is a secondary tag only.
Browse all 12 canonical works
| Method | Paper | Venue | Code | Guide |
|---|---|---|---|---|
| Evo-Reward | Genetic Programming for Reward Function Search | IEEE TAMD 2010 | Not found | Not found |
| Co-Evolved Shaping Rewards | Co-Evolution of Shaping Rewards and Meta-Parameters in Reinforcement Learning | Adaptive Behavior 2008 | Not found | Not found |
| Evolutionary Intrinsic Motivation | Intrinsically Motivated Reinforcement Learning: An Evolutionary Perspective | IEEE TAMD 2010 | Not found | Not found |
| GP End-Goal Reward | Breaking Free from Hand-Crafted Rewards: A Genetic Programming Framework for End-Goal-Driven Reinforcement Learning | IEEE CEC 2026 | Not found | Not found |
| Method | Paper | Venue | Code | Guide |
|---|---|---|---|---|
| EvoGym | Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots | NeurIPS 2021 | EvolutionGym/evogym | 中文 1 · Core 中文 2 · Core EN 1 · Core EN 2 · Extended |
| HERD | Leveraging Hyperbolic Embeddings for Coarse-to-Fine Robot Design | ICLR 2024 | drdh/HERD | EN · Extended |
| AIEA | Rapidly Evolving Soft Robots via Action Inheritance | IEEE TEVC 2024 | HandingWangXDGroup/AIEA | Not found |
| DERL | Embodied Intelligence via Learning and Evolution | Nature Communications 2021 | agrimgupta92/derl | 中文 · Core EN 1 · Core EN 2 · Extended |
| TAME | Task-Agnostic Morphology Evolution | ICLR 2021 | jhejna/morphology-opt | Not found |
| Encoding study | How the Morphology Encoding Influences the Learning Ability in Body-Brain Co-Optimization | GECCO 2023 | Not found | EN · Extended |
| Method | Paper | Venue | Code | Guide |
|---|---|---|---|---|
| XCS | Classifier Fitness Based on Accuracy | Evolutionary Computation 1995 | community implementation | EN · Extended |
| XCSG / XCSRG | Gradient Descent Methods in Learning Classifier Systems: Improving XCS Performance in Multistep Problems | IEEE TEVC 2005 | Not found | Not found |
| XCSF | Classifiers That Approximate Functions | Natural Computing 2002 | Not found | Not found |
| XCSF with tile coding | XCSF with Tile Coding in Discontinuous Action-Value Landscapes | Evolutionary Intelligence 2015 | Not found | Not found |
| DGP-XCSF | Dynamical Genetic Programming in XCSF | Evolutionary Computation 2013 | Not found | Not found |
| Resource | Scope | Paper | Code | Guide |
|---|---|---|---|---|
| EvoRL | GPU-accelerated ERL, EC, AutoRL, and RL workflows in JAX | EvoRL: A GPU-Accelerated Framework for ERL, ACM TELO 2025 | EMI-Group/evorl | Not found |
| EvoX | Distributed GPU-accelerated evolutionary computation | Documentation | EMI-Group/evox | Not found |
| QDax | Quality-Diversity and neuroevolution in JAX | QDax | adaptive-intelligent-robotics/QDax | Not found |
| QD skill discovery | Comparing neuroevolution and RL for skill discovery | Paper | instadeepai/qd-skill-discovery-benchmark | Not found |
| EvoGym | Co-design benchmark for soft robots | Paper | EvolutionGym/evogym | 中文 1 · Core 中文 2 · Core EN 1 · Core EN 2 · Extended |
| PBRL GPU Benchmark | GPU-accelerated population-based RL evaluation across robotic tasks | Benchmarking Population-Based Reinforcement Learning across Robotic Tasks with GPU-Accelerated Simulation, IEEE CASE 2025 | Asad-Shahid/PBRL | Not found |
These are non-canonical implementation, benchmark, or tooling resources and do not enter the 270-work or code-first counts. EvoGym is cross-listed with Morphological Evolution and counted once there.
Accepted papers are separated from canonical preprints and the non-counting watchlist. CORE and LaRes are cross-listed here for recency and are counted only in their primary synergistic branches.
| Method | Paper | Venue | Code | Guide |
|---|---|---|---|---|
| ERLAP | Evolutionary Reinforcement Learning with Parameterized Action Primitives for Diverse Manipulation Tasks | AAAI 2025 | Not found | Not found |
| CORE | CORE: Collaborative Optimization with Reinforcement Learning and Evolutionary Algorithm for Floorplanning | NeurIPS 2025 | yeshenpy/CORE | 中文 · Core EN · Extended |
| LaRes | LaRes: Evolutionary Reinforcement Learning with LLM-Based Adaptive Reward Search | NeurIPS 2025 | yeshenpy/LaRes | 中文 · Core EN · Extended |
| Nevo-CRL | Neuro-Evolutionary Continual Reinforcement Learning | ICML 2026 Spotlight | yeshenpy/Nevo-CRL | EN · Extended |
| HELIX | Evolutionary Reinforcement Learning for Open-Ended Scientific Problem Solving | ICLR 2026 | Not found | Not found |
| JEDi | Quality with Just Enough Diversity in Evolutionary Policy Search | GECCO 2024 | Not found | EN · Extended |
| Method | Paper | Status | Code | Guide |
|---|---|---|---|---|
| Differentiable Evolutionary Reinforcement Learning | arXiv:2512.13399 | Preprint | sitaocheng/DERL | EN · Extended |
| Lifelong Control through Neuro-Evolution | OpenReview | Submitted work | Not found | Not found |
- Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms
- Evolutionary Reinforcement Learning: A Survey
- Reinforcement Learning-Assisted Evolutionary Algorithm: A Survey and Research Opportunities
- Combining Evolution and Deep Reinforcement Learning for Policy Search: A Survey
Please open an issue or pull request with:
- the paper title and BibTeX;
- an official paper URL (publisher, proceedings, arXiv, or OpenReview);
- an author-maintained code URL, if available;
- one taxonomy branch from this README;
- one or two sentences explaining the EA-RL interaction.
To keep the list reliable, unofficial mirrors, generic search pages, and paper pages mislabeled as code will not be added.
@article{li2025bridging,
author = {Pengyi Li and Jianye Hao and Hongyao Tang and Xian Fu and Yan Zheng and Ke Tang},
title = {Bridging Evolutionary Algorithms and Reinforcement Learning: A Comprehensive Survey on Hybrid Algorithms},
journal = {IEEE Transactions on Evolutionary Computation},
year = {2025},
volume = {29},
number = {5},
pages = {1707--1728},
doi = {10.1109/TEVC.2024.3443913}
}
