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今天的主题是：Deep Reinforcement Learning Without Experience Replay, Target Networks, or Batch Updates

Summary

This research paper introduces stream-x algorithms, a novel class of deep reinforcement learning algorithms designed for streaming data. Unlike traditional deep RL methods that rely on computationally expensive batch updates and experience replay, stream-x processes individual samples in real time. The authors address the "stream barrier"—the instability and learning failures common in streaming deep RL—through several techniques including a novel optimizer, data scaling, and sparse initialization. Experiments across various benchmark environments demonstrate that stream-x algorithms achieve comparable sample efficiency and performance to batch methods, sometimes surpassing them. The study challenges the prevailing assumption that streaming deep RL is inherently sample-inefficient.

原文链接：https://openreview.net/forum?id=yqQJGTDGXN