用不确定性感知网络提升生成流模型的探索效率
Improved Exploration in GFlownets via Enhanced Epistemic Neural Networks
- 引入认知神经网络增强联合预测能力
- 在网格与序列生成任务中提升轨迹识别效率
- 适合需高效探索的组合优化场景
GFlowNets在训练过程中高效识别合适轨迹仍面临挑战，关键在于优先探索奖励分布尚未充分学习的状态空间区域。为此，需要基于不确定性的探索机制，使智能体能够感知自身的知识盲区。这一属性可通过联合预测进行度量，尤其适用于组合与序列决策问题。本文将认知神经网络（ENN）集成到传统GFlowNets架构中，实现更高效的联合预测与更优的不确定性量化，从而提升探索性能并更好识别最优轨迹。所提出的算法ENN-GFN-Enhanced在网格环境和多种设定下的结构化序列生成任务中与基线方法对比，验证了其有效性和效率。
原文摘要 · Abstract (English)
Efficiently identifying the right trajectories for training remains an open problem in GFlowNets. To address this, it is essential to prioritize exploration in regions of the state space where the reward distribution has not been sufficiently learned. This calls for uncertainty-driven exploration, in other words, the agent should be aware of what it does not know. This attribute can be measured by joint predictions, which are particularly important for combinatorial and sequential decision problems. In this research, we integrate epistemic neural networks (ENN) with the conventional architecture of GFlowNets to enable more efficient joint predictions and better uncertainty quantification, thereby improving exploration and the identification of optimal trajectories. Our proposed algorithm, ENN-GFN-Enhanced, is compared to the baseline method in GFlownets and evaluated in grid environments and structured sequence generation in various settings, demonstrating both its efficacy and efficiency.
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