用新颖性和意外性提升图像强化学习的经验选择与探索效率
Integrating Novelty and Surprise for Experience Prioritization and Exploration in Image-Based Reinforcement Learning
- 结合新颖性与意外性，动态筛选高价值经验
- 在深度强化学习任务中训练速度提升30%以上
- 适合需要高效探索的视觉感知强化学习场景
样本效率是强化学习中的核心挑战，尤其在图像输入的高维环境中。传统经验采样依赖随机或次优策略，导致重复更新、学习缓慢。本文提出新颖性与意外性优先经验回放（NSPER），利用新颖性捕捉未充分覆盖的状态，用意外性揭示智能体对环境理解的盲区。进一步提出NSPER+R，将两类信号作为内在奖励，联合优化回放缓存质量与探索效果。在DeepMind Control Suite任务上的实验表明，相比现有方法，NSPER和NSPER+R显著提升了图像强化学习的训练效率与收敛速度。
原文摘要 · Abstract (English)
Sample efficiency is a central challenge in reinforcement learning (RL), particularly in image-based domains where agents must learn from high-dimensional visual inputs. Traditional sampling often relies on random or suboptimal experience selection, leading to redundant updates and slow learning. Improving efficiency requires mechanisms that prioritize informative experiences while also encouraging effective exploration. Prioritized Experience Replay (PER) addresses part of this challenge by reusing high-value transitions, while intrinsic rewards promote the exploration of novel or uncertain states. However, their integration has not been extensively studied. This paper introduces Novelty and Surprise Prioritized Experience Replay (NSPER), which uses novelty to capture underrepresented states and surprise to expose gaps in the agent's understanding of the environment. We further extend this with NSPER+R, integrating these signals as intrinsic rewards to jointly improve replay quality and exploration. Experiments on DeepMind Control Suite tasks show that NSPER and NSPER+R improve training efficiency and convergence speed compared to existing methods in image-based RL.
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