Reachability Objective

Reachability objectives in planning and control focus on maximizing the probability of reaching a desired state or satisfying a temporal goal, often within a stochastic or partially observable environment. Current research emphasizes efficient algorithms, such as heuristic search value iteration and adaptations of reinforcement learning, to solve these problems within various model architectures including Markov Decision Processes (MDPs) and Partially Observable Markov Decision Processes (POMDPs). This area is crucial for developing robust and safe autonomous systems, particularly in applications requiring guaranteed satisfaction of temporal logic specifications or safety constraints, as seen in robotics and other domains.