Reinforcement Learning Scaling with RLlib and Ray

Two Sigma is developing capabilities to scale reinforcement learning (RL) training. This involves leveraging RLlib and its integration with Ray to distribute RL workloads across a cluster of commodity machines, significantly reducing training wall-time by overcoming CPU-bound bottlenecks in sample generation from environments. This approach enables orders of magnitude speedups in RL agent training. This post details how scaling RLlib with Ray can cut training wall-time down by orders of magnitude by removing CPU bottlenecks.