HD-CB: The First Exploration of Hyperdimensional Computing for Contextual Bandits Problems

Hyperdimensional Computing (HDC), or Vector Symbolic Architectures, is a computing paradigm that combines symbolic reasoning with the efficiency of distributed representations. HDC offers energy and computational efficiency, inherent parallelism, and robustness, making it a promising alternative for learning tasks in resource-constrained environments and hardware acceleration. This work introduces Hyperdimensional Contextual Bandits (HD-CB), the first exploration of HDC for modeling and solving Contextual Bandits (CB) tasks. CBs are sequential decision-making problems where, at each iteration, an agent observes a context, selects an action from a finite set, and receives a reward, aiming to maximize cumulative returns over time. The proposed HD-CB framework encodes contextual information in a high-dimensional space and represents each action with a dedicated hypervector. Action selection and learning are performed using HDC arithmetic, replacing the computationally expensive ridge regression used in traditional linear CB algorithms with simple, highly parallel vector operations. We propose four HD-CB variants with different exploration strategies and update rules. Experiments on synthetic datasets and the MovieLens-100 k benchmark show that HD-CB achieves comparable or superior accuracy to linear CB methods, improves convergence speed, scalability, memory efficiency, and emerges as a perfect candidate for hardware acceleration, with up to 141.7× speedup.