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Kolmogorov-Arnold Network for Online Reinforcement Learning

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arxiv 2408.04841 v3 pith:7ZSYUAC3 submitted 2024-08-09 cs.LG cs.AI

Victor Augusto Kich , Jair Augusto Bottega , Raul Steinmetz , Ricardo Bedin Grando , Ayano Yorozu , Akihisa Ohya This is my paper · ORCID
classification cs.LGcs.AI
keywords kanslearningreinforcementalgorithmfewerfunctionkolmogorov-arnoldmlp-based
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Kolmogorov-Arnold Networks (KANs) have shown potential as an alternative to Multi-Layer Perceptrons (MLPs) in neural networks, providing universal function approximation with fewer parameters and reduced memory usage. In this paper, we explore the use of KANs as function approximators within the Proximal Policy Optimization (PPO) algorithm. We evaluate this approach by comparing its performance to the original MLP-based PPO using the DeepMind Control Proprio Robotics benchmark. Our results indicate that the KAN-based reinforcement learning algorithm can achieve comparable performance to its MLP-based counterpart, often with fewer parameters. These findings suggest that KANs may offer a more efficient option for reinforcement learning models.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. KANEL\'E: Kolmogorov-Arnold Networks for Efficient LUT-based Evaluation

    cs.AR 2025-12 conditional novelty 7.0 of 10

    Quantized, pruned Kolmogorov-Arnold Networks can be compiled directly into FPGA lookup tables, achieving extreme latency/resource reductions and matching state-of-the-art LUT-based networks on several benchmarks.

  2. CIKAN: Constraint Informed Kolmogorov-Arnold Networks for Autonomous Spacecraft Rendezvous using Time Shift Governor

    eess.SY 2024-12 conditional novelty 5.0 of 10

    CIKAN, a KAN-based constraint-informed network, approximates the Time Shift Governor for spacecraft rendezvous and, in simulation, enforces constraints while reducing average computation time and fuel use relative to ...

  3. Agile Reinforcement Learning through Separable Neural Architecture and Applications

    cs.LG 2026-01 conditional novelty 4.0 of 10

    SPAN, a KHRONOS-derived spline network with a learnable preprocessing layer, reports better sample efficiency and success rates than small MLPs across several RL benchmarks, though the abstract overclaims and omits th...

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