AgileRL/AgileRL
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.
What it solves
AgileRL is designed to streamline deep reinforcement learning (RL) by introducing RLOps, reducing the time and effort required for training models and hyperparameter optimization (HPO). It specifically addresses the inefficiency of traditional HPO, which often requires hundreds of separate training runs to find optimal settings.
How it works
The library uses evolutionary HPO techniques to automatically converge on optimal hyperparameters. Instead of running multiple independent experiments, it trains a population of agents that share experiences but evolve and mutate their hyperparameters over time. It supports a wide range of RL paradigms, including on-policy, off-policy, offline, multi-agent, and contextual multi-armed bandits, and provides a LocalTrainer for easy setup or a managed cloud platform called Arena for distributed training.
Who it’s for
It is intended for developers and researchers working with reinforcement learning who want to faster training cycles and automated hyperparameter tuning without the manual trial-and-error process.
Highlights
- Evolutionary HPO: Claims 10x faster hyperparameter optimization than state-of-the-art methods by removing the need for multiple training runs.
- Broad Algorithm Support: Includes PPO, DQN, Rainbow DQN, DDPG, TD3, CQL, ILQL, MADDPG, MATD3, and IPPO.
- LLM Fine-tuning: Supports evolvable LLM reinforcement fine-tuning for long-horizon tasks.
- RLOps Platform: Integrates with Arena, a managed cloud infrastructure for validating custom environments and deploying agents.
- Flexible Training: Offers both simple high-level APIs and custom training pipelines for full control over networks and mutation strategies.