MENADD - Introduction to Reinforcement Learning

In my presentation on Introduction to Reinforcement Learning, I provided the audience with an accessible overview of this exciting field in artificial intelligence. I began by explaining the fundamental concepts of reinforcement learning, where agents learn to make decisions through interactions with an environment, receiving rewards or penalties based on their actions. The audience learned about the key components of reinforcement learning, including states, actions, and rewards, and how they form the basis of the learning process. I introduced popular algorithms like Q-Learning and Deep Q Networks (DQN), showcasing how they enable agents to learn optimal strategies in complex environments. Practical examples and simulations further illustrated the potential of reinforcement learning in solving real-world problems, ranging from game playing to robotic control. By the end of the talk, the audience gained a foundational understanding of reinforcement learning and left inspired to explore its applications in various domains.