Improved Exploration in GFlownets via Enhanced Epistemic Neural Networks
Muhammad, Sajan Lahlou, Salem
Muhammad, Sajan
Lahlou, Salem
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Machine Learning
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Conference proceeding
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Abstract
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.
Citation
S. Muhammad, S. Lahlou, "Improved Exploration in GFlownets via Enhanced Epistemic Neural Networks," 2026, pp. 115-122.
Source
2025 7th International Conference on Intelligent Autonomous Systems (ICoIAS)
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7th International Conference on Intelligent Autonomous Systems (ICoIAS)
Keywords
46 Information and Computing Sciences, 4611 Machine Learning
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7th International Conference on Intelligent Autonomous Systems (ICoIAS)
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IEEE