Breaking the Grid: Distance-Guided Reinforcement Learning in Large Discrete Action Spaces

Researchers have introduced Distance-Guided Reinforcement Learning (DGRL), a novel algorithm designed to address the curse of dimensionality in large-scale decision-making problems. Traditional reinforcement learning methods often struggle with massive discrete action spaces common in logistics, scheduling, and recommender systems. DGRL overcomes these limitations by combining Sampled Dynamic Neighborhoods with Distance-Based Updates, enabling efficient processing for problems involving up to 10^20 actions. This approach transforms policy optimization into a stable regression task, effectively decoupling gradient variance from action space cardinality. The study demonstrates that DGRL provides provable guarantees for local value improvement on structured tasks and naturally extends to hybrid continuous-discrete action spaces. Experimental results indicate performance improvements of up to 66% compared to state-of-the-art benchmarks across various environments, alongside enhanced convergence speed and reduced computational complexity. This advancement represents a significant step forward in applying reinforcement learning to complex, high-dimensional real-world applications.