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In Ant-v5, retaining PPO's parameters boosts early returns and remains the safest choice across all faults, while retaining SAC's parameters yields m","title":"Enhancing Hardware Fault Tolerance in Machines with Reinforcement Learning Policy Gradient Algorithms","url":"https://arxiv.org/abs/2407.15283","vendor":"arxiv_cs_ai"},"summary":"arXiv:2407.15283v2 Announce Type: replace-cross \nAbstract: Industry is moving toward autonomous, network-connected machines that detect and adapt to changing conditions, including hardware faults. Conventional fault-tolerant design duplicates hardware and reroutes control logic; reinforcement learning (RL) offers a learning-based alternative. This paper presents the first systematic comparison of two RL algorithms -- Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC) -- for integrating fault tolerance into control. 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