Not All Transitions Matter: Evidence from PPO
Researchers have developed a method to improve the stability of reinforcement learning training by randomly dropping a fraction of transitions from on-policy rollouts. This technique, applied to Proximal Policy Optimization (PPO), breaks the repetitive gradient structure caused by causally chained states. By dropping approximately 25% of transitions, the method maintains reward performance while yielding more consistent training dynamics across various metrics. AI
IMPACT Enhances training stability for reinforcement learning agents, potentially leading to more reliable and efficient development of AI systems in complex environments.