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SAC-TD3 Hybrid is an algorithm which has been used to estimate the reaction barrier given a potential energy surface. It incorporates elements from both the TD3 and the SAC algorithms. Stable states in complex systems correspond to local minima on the associated potential energy surface, transitions between which govern the dynamics of the system. Precisely determining the transition pathways in complex and high-dimensional systems is challenging because these transitions are rare events, and the system remains near a local minimum for most of the time. The probability of such transitions decreases exponentially with the height of the energy barrier, making the system's dynamics highly sensitive to the calculated energy barriers. This problem has is formulated as a cost-minimization problem and solved using the above mentioned reinforcement learning algorithm. It incorporates the idea of entropy regularization from SAC while borrowing target policy smoothening, delayed policy updates from the TD3 algorithm. The exploratory nature of the algorithm enables efficient sampling and better estimation of the minimum energy barrier for transitions.
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deep reinforcement learning - techniques combining deep learning and reinforcement learning principles to create efficient machine learning algorithms
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reinforcement learning - field of machine learning
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algorithm - sequence of instructions to perform a task
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