Mesh-RL Accelerates Reinforcement Learning with Spatial Decomposition

Reinforcement learning (RL) in large or sparse-reward environments often struggles with slow credit assignment, as value information propagates only locally across the state space. To address this, researchers have introduced Mesh-RL, a novel spatial domain-decomposition framework. Inspired by finite element methods and domain decomposition theory from scientific computing, Mesh-RL divides the environment into overlapping subgrids. The core innovation of Mesh-RL lies in its ability to enforce boundary-consistent temporal-difference updates. This mechanism allows for localized learning within each subgrid while simultaneously ensuring that value information remains globally coherent across the entire environment. Unlike hierarchical or model-based RL approaches, Mesh-RL achieves faster long-range credit assignment without altering the reward function, Bellman operator, or introducing explicit planning. Evaluations on hazard-dense grid-world environments demonstrated that Mesh-RL consistently enhances convergence speed, cumulative reward, and learning stability across various RL algorithms like Q-learning, SARSA, and Dyna-Q. Higher mesh resolutions were shown to sustain exploration, prevent premature convergence, and substantially accelerate value propagation to distant states, bridging scientific computing techniques with RL to improve sample efficiency.