Distributed Reinforcement Learning for Scalable High-Performance Policy Optimization

Applying reinforcement learning to real-world problems is notoriously difficult because, unlike controlled simulations, environments are noisy, rewards are ambiguous, and mistakes have significant consequences. Despite these challenges, distributed systems have enabled AI to achieve superhuman performance in complex games like Dota 2 and StarCraft II. This advanced approach relies on numerous "actors" simultaneously gathering experience in parallel copies of an environment. A central learner then aggregates this data and uses the Proximal Policy Optimization (PPO) algorithm to make stable, incremental improvements to the shared policy.