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تسجيل مستخدم جديدRealAnt: An Open-Source Low-Cost Quadruped for Research in Real-World Reinforcement Learning
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Current robot platforms available for research are either very expensive or unable to handle the abuse of exploratory controls in reinforcement learning. We develop RealAnt, a minimal low-cost physical version of the popular Ant benchmark used in reinforcement learning. RealAnt costs only $410 in materials and can be assembled in less than an hour. We validate the platform with reinforcement learning experiments and provide baseline results on a set of benchmark tasks. We demonstrate that the TD3 algorithm can learn to walk the RealAnt from less than 45 minutes of experience. We also provide simulato
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