AgileRL
Active·★ 921·Updated 2026-05-29
★ Trending★ Hidden Gem
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.
AgileRL is a Deep Reinforcement Learning library that streamlines development by introducing RLOps, or MLOps for reinforcement learning. It significantly reduces training time and hyperparameter optimization using pioneering evolutionary techniques, offering up to 10x faster optimization than state-of-the-art methods.
#Reinforcement Learning#Deep Learning#Hyperparameter Optimization#RLOps#Evolutionary Algorithms#Data Analysis
01
Features
01RLOps integration for streamlined reinforcement learning development.
02Pioneering evolutionary hyperparameter optimization (HPO) techniques.
03Comprehensive suite of evolvable on-policy, off-policy, offline, multi-agent, and contextual multi-armed bandit algorithms.
04Support for distributed training.
05Algorithms for Large Language Model (LLM) finetuning.
02
Compatibility
Gymnasium
Supported
Verified via docs
PettingZoo
Supported
Verified via docs
PyTorch
Native
Verified via docs
LLM Ecosystem
Supported
Verified via docs
03
Quick start
1
$ pip install agilerl
04
Use cases
↳Training single-agent tasks in standard Gymnasium environments.
↳Developing multi-agent reinforcement learning solutions in PettingZoo environments.
↳Fine-tuning Large Language Models (LLMs) with reinforcement learning algorithms.
05
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