Action-constrained reinforcement learning (ACRL) is a popular approach for solving safety-
critical and resource-allocation related decision making problems. A major challenge in …

Tractable Boolean and arithmetic circuits have been studied extensively in AI for over two
decades now. These circuits were initially proposed as “compiled objects,” meant to facilitate …

Action-constrained reinforcement learning (ACRL), where any action taken in a state must
satisfy given constraints, has several practical applications such as resource allocation in …

Deep neural networks (DNN) can approximate value functions or policies for reinforcement
learning, which makes the reinforcement learning algorithms more powerful. However, some …

多智能体路径规划(multi-agent path finding, MAPF) 是为多个智能体规划路径的问题,
关键约束是多个智能体同时沿着规划路径行进而不会发生冲突. MAPF 在物流, 军事 …

Many real-world problems, such as probabilistic reasoning, can be formulated as the task of
counting the models of a propositional formula, called# SAT. A model of a formula is an …

Action-constrained reinforcement learning (ACRL) is a popular approach for solving safety-
critical and resource-allocation related decision making problems. A major challenge in …

Action-constrained reinforcement learning (ACRL) is a popular approach for solving safety-
critical and resource-allocation related decision making problems. A major challenge in …