Reinforcement learning for hexapod robot trajectory control: a study of Q-learning and SARSA algorithms

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dc.contributor.authorAhmed Benyoucef
dc.contributor.authorYoucef Zennir
dc.contributor.authorAmmar Belatreche
dc.contributor.authorManuel F. Silva
dc.contributor.authorMohamed Benghanem
dc.date.accessioned2026-01-27T09:35:10Z
dc.date.available2026-01-27T09:35:10Z
dc.date.issued2025-10-25
dc.description.abstractHexapod robots, with their six-legged design, excel in stability and adaptability on challenging terrain but pose signifi cant control challenges due to their high degrees of freedom. While reinforcement learning (RL) has been explored for robot navigation, few studies have systematically compared on-policy and off-policy methods for multi-legged locomo tion. This work presents a comparative study of SARSA and Q-Learning for trajectory control of a simulated hexapod robot, focusing on the influence of learning rate (α), discount factor (γ), and eligibility trace (λ). The evaluation spans eight initial poses, with performance measured through lateral deviation (Ey), orientation error (Eθ), and iteration count. Results show that Q-Learning generally achieves faster convergence and greater stability, particularly with higher γ and λ values, while SARSA can achieve competitive accuracy with careful parameter tuning. The findings demonstrate that eligibility traces substantially improve learning precision and provide practical guidelines for robust RL-based control in multi-legged robotic systems.
dc.identifier.urihttp://dspace.univ-skikda.dz:4000/handle/123456789/5819
dc.language.isoen
dc.publisherInternational Journal of Intelligent Robotics and Applications
dc.titleReinforcement learning for hexapod robot trajectory control: a study of Q-learning and SARSA algorithms
dc.typeArticle
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