👉 RT (Reinforcement Theory) research in artificial intelligence, particularly in the context of machine learning and robotics, focuses on how agents learn to make decisions by interacting with their environment to maximize cumulative rewards. This theory draws parallels between reinforcement learning algorithms used in AI and human learning processes, where actions are guided by feedback in the form of rewards or penalties. Researchers explore various strategies for optimizing these learning processes, including deep reinforcement learning, which combines neural networks with reinforcement learning to handle complex, high-dimensional state spaces. The goal is to develop systems that can adapt and improve their performance over time without explicit programming, enabling applications in areas like autonomous vehicles, game playing, and personalized recommendation systems. RT research aims to bridge the gap between theoretical models and practical implementations, enhancing the efficiency and effectiveness of AI systems in dynamic and uncertain environments.
