通过识别稀有状态，让强化学习更高效复用数据。
Novelty-based Sample Reuse for Continuous Robotics Control
- 根据状态罕见程度决定是否重用数据，稀有状态多更新，常见状态跳过。
- 在不显著增加耗时的前提下，提升算法收敛速度与成功率达20%以上。
- 适合需要高效训练的机器人控制任务，尤其在仿真环境应用佳。
在强化学习中，智能体通过与环境交互收集状态信息和奖励，这对策略优化至关重要，但过程耗时较长，尤其在复杂的机器人仿真和真实应用中。传统算法通常在处理完一批样本后才重新与环境互动，未能充分利用历史数据。频繁出现的状态价值估计已较可靠，无需过多更新；而稀有状态则需更密集的更新以获得准确估值。为解决样本利用不均的问题，我们提出新颖性引导的数据重用方法（Novelty-guided Sample Reuse, NSR）。NSR对罕见、新颖的状态提供额外更新，对频繁状态跳过重复更新，从而在下一次环境交互前最大化样本利用率。实验表明，NSR在不显著增加时间消耗的情况下，提升了算法的收敛速度与成功率。代码开源地址：https://github.com/ppksigs/NSR-DDPG-HER。
原文摘要 · Abstract (English)
In reinforcement learning, agents collect state information and rewards through environmental interactions, essential for policy refinement. This process is notably time-consuming, especially in complex robotic simulations and real-world applications. Traditional algorithms usually re-engage with the environment after processing a single batch of samples, thereby failing to fully capitalize on historical data. However, frequently observed states, with reliable value estimates, require minimal updates; in contrast, rare observed states necessitate more intensive updates for achieving accurate value estimations. To address uneven sample utilization, we propose Novelty-guided Sample Reuse (NSR). NSR provides extra updates for infrequent, novel states and skips additional updates for frequent states, maximizing sample use before interacting with the environment again. Our experiments show that NSR improves the convergence rate and success rate of algorithms without significantly increasing time consumption. Our code is publicly available at https://github.com/ppksigs/NSR-DDPG-HER.
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