Explore fundamental concepts of Deep Reinforcement Learning in this comprehensive lecture that delves into the core principles of Reinforcement Learning, including Policy iteration and value iteration methodologies. Master the theoretical foundations of Markov decision process (MPD), understand the implementation of Monte Carlo estimation techniques, and learn about temporal difference algorithms. Through this 100-minute session, gain essential knowledge that serves as a crucial foundation for advanced applications in Deep Reinforcement Learning and artificial intelligence systems.