AgileRL is a deep reinforcement learning library designed to accelerate RL development through RLOps, focusing on reducing training and hyperparameter optimization (HPO) times. It targets researchers and practitioners seeking efficient RL model development, offering state-of-the-art algorithms and pioneering evolutionary HPO techniques for significant speedups.

How It Works

AgileRL leverages evolutionary algorithms for hyperparameter optimization, automating the discovery of optimal configurations. This approach contrasts with traditional methods requiring numerous manual training runs. The library supports distributed training and includes a range of on-policy, off-policy, offline, multi-agent, and contextual multi-armed bandit algorithms, all designed to be "evolvable."

Quick Start & Requirements

• Install: pip install agilerl
• Prerequisites: Python 3.x, PyTorch. GPU with CUDA is recommended for performance.
• Documentation: https://docs.agilerl.com/en/latest/
• Demo/Platform: https://arena.agilerl.com

Highlighted Details

• Claims 10x faster hyperparameter optimization compared to traditional RL frameworks combined with Optuna.
• Supports multi-agent RL using Petting Zoo-style API, with benchmarks against other libraries.
• Implements a wide array of algorithms including PPO, DQN, Rainbow DQN, DDPG, TD3, CQL, ILQL, MADDPG, MATD3, NeuralUCB, and NeuralTS.
• Features include custom module/network creation and LLM finetuning capabilities.

Maintenance & Community

• Active development with a recent release of AgileRL 2.0.
• Community support available via Discord: https://discord.gg/eB8HyTA2ux

Licensing & Compatibility

• License: Apache 2.0.
• Compatibility: Permissive license suitable for commercial use and integration with closed-source projects.

Limitations & Caveats

The library is under active development, with "more coming soon" for evolvable algorithms. While benchmarks claim significant speedups, real-world performance may vary based on specific environments and configurations.