RL Concepts
RL Concepts refers to the foundational principles and methodologies of Reinforcement Learning (RL), a subset of machine learning where an agent learns to make decisions by interacting with an environment. Key concepts include rewards, states, actions, policies, and value functions, which guide the agent's learning process to maximize cumulative reward. RL is used in diverse applications, from robotics and autonomous vehicles to game playing and optimization problems. This tag is essential for researchers, developers, and students interested in understanding how agents learn through trial and error. Engaging with RL Concepts fosters a deeper understanding of this dynamic and evolving area of AI.
