RLlib: Scalable RL for TensorFlow, PyTorch, and Beyond
summary
RLlib presents a unified API and cluster runtime that runs TensorFlow and PyTorch at scale, with RLlib 1.0 claiming 100% PyTorch parity, NVF-MPO and Dreamer implemented, and Neural MMO reaching new state-of-the-art just by scaling. A new distributed execution DSL and trajectory view API simplify writing algorithms: APEX/IMPALA implementations shrink from ~400–500 lines to ~100–200, a >2x code reduction, and a 1,000+ user Slack and growing GitHub issues show active use.
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chapters (9)
- 0:00Intro
- 0:30Overview
- 0:49Why reinforcement learning?
- 2:27What problem does RLlib solve?
- 4:15] Unified API for apps
- 6:32] RLlib reference algorithms Cohesive API across
- 7:27] RLlib distributed execution
- 10:31Community Update
- 13:31Summary
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