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RL-Swarm: 开源分布式强化学习训练框架
RL-Swarm is an open-source framework designed for building and managing distributed reinforcement learning training environments across the internet. It enables researchers and engineers to train complex agents at scale by leveraging a swarm of distributed computing resources.
Python
Added on 2025年6月30日960
Stars418
ForksPython
LanguageProject Introduction
Summary
RL-Swarm is a novel open-source framework that simplifies the process of conducting large-scale reinforcement learning experiments by distributing the training workload across a network of machines, effectively creating a 'swarm' for computation.
Problem Solved
Training modern reinforcement learning models often requires significant computational power and can be limited by the resources of a single machine. Scaling training across a network can be complex due to network latency, node heterogeneity, and potential failures. RL-Swarm provides a simplified way to harness distributed power.
Core Features
Distributed Training Orchestration
Orchestrate training tasks across multiple machines connected over the internet.
Fault Tolerance
Handles node failures and network inconsistencies to ensure training progress.
Internet-Scale Connectivity
Provides mechanisms for connecting and managing a large number of diverse computing nodes.
Flexible Agent/Environment Integration
Offers clear interfaces for integrating custom reinforcement learning agents and environments.
Tech Stack
Python
Asyncio
gRPC
Kubernetes (for orchestration examples)
Docker
使用场景
RL-Swarm is suitable for various applications requiring scalable and distributed reinforcement learning training.
场景一：大规模游戏 AI 训练
Details
Train complex game playing agents (e.g., AlphaGo-like systems) by distributing simulations and policy updates across a large number of machines.
User Value
Significantly reduce the time required to train highly performant game AI, enabling faster iteration and exploration of complex strategies.
场景二：分布式超参数调优
Details
Optimize hyperparameters for reinforcement learning algorithms by running many training instances in parallel on different nodes with varied configurations.
User Value
Efficiently find optimal model configurations, leading to improved agent performance without extensive manual trial and error.
场景三：复杂仿真环境训练
Details
Train agents for complex physical or environmental simulations that are computationally expensive to run on a single machine.
User Value
Enable the training of agents in high-fidelity simulations by distributing the simulation workload, making previously intractable problems solvable.
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