Action Selection

Action selection, the process by which agents choose actions to achieve goals, is a central problem in artificial intelligence and robotics. Current research focuses on improving action selection efficiency and robustness using various approaches, including Bayesian networks, Markov Decision Processes (MDPs), and reinforcement learning (RL) algorithms enhanced with techniques like extremum-seeking control and Monte Carlo tree search. These advancements aim to address challenges such as uncertainty, safety constraints, and dynamic environments, leading to more effective and reliable autonomous systems in diverse applications like robotics, control systems, and human-robot interaction.