sub:assertion {
  <https://w3id.org/kpxl/ios/ds/terms/SAC-TD3_Hybrid> a <http://www.w3.org/2002/07/owl#Class> ;
    rdfs:label "SAC-TD3 Hybrid" ;
    rdfs:subClassOf <http://www.wikidata.org/entity/Q8366> ;
    <http://www.w3.org/2002/07/owl#sameAs> <https://w3id.org/np/RAgOeYsOZ9JMrf7eZXm9LfwjGMukSuzSpk6eeHFRpRlNo#SAC-TD3Hybrid> ;
    <http://www.w3.org/2004/02/skos/core#definition> "SAC-TD3 is an algorithm incorporating elements both from the SAC and TD3 algorithms for reinforcement learning. It incorporates the idea of entropy regularization from SAC while borrowing target policy smoothening, delayed policy updates from the TD3 algorithm. It has been used to estimate the reaction barrier given a potential energy surface. 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 aforementioned reinforcement learning algorithm. The exploratory nature of the algorithm enables efficient sampling and better estimation of the minimum energy barrier for transitions." ;
    <http://www.w3.org/2004/02/skos/core#relatedMatch> <http://www.wikidata.org/entity/Q65079156> , <http://www.wikidata.org/entity/Q830687> .
}