Deep Reinforcement Learning sits at the intersection of deep learning and reinforcement learning, combining the powerful representational capabilities of neural networks with the decision-making framework of RL. This synergy has led to breakthroughs in various domains, from mastering complex games like AlphaGo and Dota 2 to controlling robotic systems and optimizing industrial processes.

Throughout this course, you will gain a deep understanding of the core concepts and algorithms that underpin DRL. We'll start with a refresher on fundamental reinforcement learning principles, including Markov Decision Processes (MDPs), value functions, and policy optimization. Then, we'll dive into the "deep" aspect, exploring how neural networks are used to approximate these functions, enabling agents to handle high-dimensional state and action spaces.

You'll learn about various cutting-edge DRL algorithms, including:

• Deep Q-Networks (DQN): A foundational algorithm that uses deep neural networks to estimate Q-values, enabling agents to learn optimal policies in environments with discrete action spaces.

• Policy Gradient Methods: Algorithms that directly optimize the agent's policy, allowing for more continuous control and handling of complex action spaces. We'll cover variations like REINFORCE.

• Actor-Critic Methods: These combine the strengths of value-based and policy-based methods, using a "critic" to evaluate actions and an "actor" to generate them. Algorithms like A2C and A3C will be discussed.

• Proximal Policy Optimization (PPO): A widely used and highly effective algorithm known for its stability and performance in various DRL tasks.