AgileRL/AgileRL
Streamlining reinforcement learning with RLOps. State-of-the-art RL algorithms and tools, with 10x faster training through evolutionary hyperparameter optimization.
Implements evolutionary hyperparameter optimization that automatically tunes agent networks during training through population-based mutation and selection, eliminating the need for separate HPO runs. Supports diverse RL paradigms—on-policy (PPO), off-policy (TD3, DQN), offline RL, multi-agent (MADDPG, MATD3), and contextual bandits—with distributed training across multiple workers and Petting Zoo compatibility for multi-agent environments. Also includes LLM fine-tuning capabilities via reinforcement feedback (RFT) with optional dependencies for transformers, DeepSpeed, and PEFT.
896 stars. Actively maintained with 10 commits in the last 30 days.
Stars
896
Forks
66
Language
Python
License
Apache-2.0
Category
Last pushed
Mar 09, 2026
Commits (30d)
10
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