Streaming Deep Reinforcement Learning Finally Works

Understand

Natural intelligence processes experience as a continuous stream, sensing, acting, and learning moment-by-moment in real time.

• Streaming learning, the modus operandi of classic reinforcement learning (RL) algorithms like Q-learning and TD, mimics natural learning by using the most recent sample without storing it.
• This approach is also ideal for resource-constrained, communication-limited, and privacy-sensitive applications.
• However, in deep RL, learners almost always use batch updates and replay buffers, making them computationally expensive and incompatible with streaming learning.