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Towards Data Science

Distributed reinforcement learning enables training agents for complex real-world problems by parallelizing experience collection across multiple actors and centralizing policy optimization in learners. The article covers practical implementation of actor-critic architectures using PPO, introduces V-trace for handling off-policy data in asynchronous systems (IMPALA), and demonstrates scaling to multiple machines using Redis for trajectory storage and NCCL for gradient synchronization. Key techniques include proper MDP formulation, importance sampling corrections, and distributed training infrastructure that powers systems like OpenAI Five and AlphaStar.

Distributed Reinforcement Learning for Scalable High-Performance Policy Optimization

Reinforcement Learning on Real-World Problems is Hard

A real-world reinforcement learning problem

The Distributed Actor-Learner Architecture

IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures

Massively Distributed Actor-Learner Architecture