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EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It supports Reinforcement Learning (RL), Evolutionary Computation (EC), Evolution-guided Reinforcement Learning (ERL), AutoRL, and seamless integration with GPU-optimized simulation environments.
| Date | Stars |
|---|---|
| 2026-07-31 | 344 |
| 2026-08-01 | 344 |
| 2026-08-02 | 344 |
| 2026-08-06 | 344 |
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<h1 align="center">
<a href="https://github.com/EMI-Group/evox">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="docs/_static/evox_logo_dark.svg">
<source media="(prefers-color-scheme: light)" srcset="docs/_static/evox_logo_light.svg">
<img alt="EvoX Logo" height="50" src="docs/_static/evox_logo_light.svg">
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</a>
</h1>
<p align="center">
<img src="https://github.com/google/brax/raw/main/docs/img/humanoid_v2.gif", width=160, height=160/>
<img src="https://github.com/kenjyoung/MinAtar/raw/master/img/breakout.gif", width=160, height=160>
<img src="https://raw.githubusercontent.com/instadeepai/jumanji/main/docs/env_anim/bin_pack.gif", width=160, height=160>
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<h2 align="center">
<p>🌟 EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning 🌟</p>
<a href="https://arxiv.org/abs/2501.15129">
<img src="https://img.shields.io/badge/paper-arxiv-red?style=for-the-badge" alt="EvoRL Paper on arXiv">
</a>
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# Table of Contents
- [Table of Contents](#table-of-contents)
- [Introduction](#introduction)
- [Highlight](#highlight)
- [Update](#update)
- [Documentation](#documentation)
- [Overview of Key Concepts in EvoRL](#overview-of-key-concepts-in-evorl)
- [Installation](#installation)
- [Quickstart](#quickstart)
- [Training](#training)
- [Logging](#logging)
- [Env Rendering](#env-rendering)
- [Algorithms](#algorithms)
- [RL Environments](#rl-environments)
- [Current Supported Environments](#current-supported-environments)
- [Performance](#performance)
- [Bug report \& Discussion](#bug-report--discussion)
- [Acknowledgement](#acknowledgement)
- [Citing EvoRL](#citing-evorl)
# Introduction
EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, which is implemented by JAX and provides end-to-end GPU-accelerated training pipelines, including following processes:
- Reinforcement Learning (RL)
- Evolutionary Computation (EC)
- Environment Simulation
EvoRL provides a highly efficient and user-friendly platform to develop and evaluate RL, EC and EvoRL algorithms.
> [!NOTE]
> EvoRL is a sister project of [EvoX](https://github.com/EMI-Group/evox).
## Highlight
- **End-to-end training pipelines**: The training pipelines for RL, EC and EvoRL are entirely executed on GPUs, eliminating dense communication between CPUs and GPUs in traditional implementations and fully utilizing the parallel computing capabilities of modern GPU architectures.
- Most algorithms has a `Workflow.step()` function that is capable of `jax.jit` and `jax.vmap()`, supporting parallel training and JIT on full computation graph.
- **Easy integration between EC and RL**: Due to modular design, EC components can be easily plug-and-play in workflows and cooperate with RL.
- **Implementation of EvoRL algorithms**: Currently, we provide two popular paradigms in Evolutionary Reinforcement Learning: Evolution-guided Reinforcement Learning (ERL): ERL, CEM-RL; and Population-based AutoRL: PBT.
- **Unified Environment API**: Support multiple GPU-accelerated RL environment packages (eg: Brax, gymnax, ...). Multiple Env Wrappers are also provided.
- **Object-oriented functional programming model**: Classes define the static execution logic and their running states are stored externally.
### Update
- 2025-07-14: Our paper *"EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning"* is accepted by ACM TELO.
- 2025-04-01: Add support for Mujoco Playground Environments.
## Documentation
- For comprehensive guidance, please visit our [Documentation](https://evorl.readthedocs.io/latest/), where you'll find detailed installation steps, tutorials, practical examples, and complete API references.
- EvoRL is also indexed by DeepWiki, providing an AI assistant for beginners. Feel free to ask any question about this repo at https://deepwiki.com/EMI-Group/evorl.
## Overview of Key Concepts in EvoRL
, AutoRL, and integration with GPU-optimized simulation environments.'
matched fp:8afe0c6314949bbc, llm:Topics and description: automl, evolutionary-algorithms, gpu-acceleration, hpo, jax, policy-gradient, population-based-training, ppo, reinforcement-learning, reinforcement-learning-agent, reinforcement-learning-environments, sac; description: 'EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning... supports RL, Evolutionary Computation, Evolution-guided Reinforcement Learning (ERL), AutoRL, and integration with GPU-optimized simulation environments.'
matched fp:8afe0c6314949bbc, llm:Topics and description: automl, evolutionary-algorithms, gpu-acceleration, hpo, jax, policy-gradient, population-based-training, ppo, reinforcement-learning, reinforcement-learning-agent, reinforcement-learning-environments, sac; description: 'EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning... supports RL, Evolutionary Computation, Evolution-guided Reinforcement Learning (ERL), AutoRL, and integration with GPU-optimized simulation environments.'
matched fp:8afe0c6314949bbc, llm:Topics and description: automl, evolutionary-algorithms, gpu-acceleration, hpo, jax, policy-gradient, population-based-training, ppo, reinforcement-learning, reinforcement-learning-agent, reinforcement-learning-environments, sac; description: 'EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning... supports RL, Evolutionary Computation, Evolution-guided Reinforcement Learning (ERL), AutoRL, and integration with GPU-optimized simulation environments.'