In today’s rapidly evolving technological landscape, Artificial Intelligence (AI) and Machine Learning (ML) are no longer futuristic concepts; they are integral parts of our daily lives. For professionals seeking to excel in this dynamic field, an online Master of Science in Computer Science (MS CS) degree, with a focus on areas like Reinforcement Learning (RL), presents a significant advantage. Reinforcement Learning, a core component of modern AI, is essential for developing intelligent systems capable of making decisions in complex environments, much like humans do.
This course delves into the fundamental theory and practical applications of modern reinforcement learning. It addresses the core problem of reinforcement learning: how to enable agents to learn optimal behaviors by interacting with their environment to maximize a reward signal. The curriculum is structured to provide a comprehensive understanding of both model-free and model-based RL methods, with a strong emphasis on temporal difference learning and policy gradient algorithms. These methods are at the heart of many cutting-edge applications, from robotics and autonomous systems to advanced game playing AI, such as those seen in Poker, Go, and Starcraft.
What You Will Learn
The course is designed to equip students with a robust skillset, including:
- Deep Understanding of RL Theory: Grasp the theoretical underpinnings of reinforcement learning and learn how to apply these principles to solve real-world problems.
- Policy Evaluation and Optimization: Master techniques for evaluating the effectiveness of different policies and for learning how to develop optimal policies in sequential decision-making scenarios.
- Methodological Trade-offs: Understand the distinctions and practical trade-offs between value function methods, policy search methods, and actor-critic methods within reinforcement learning.
- Model-Based vs. Model-Free Learning: Discern when and how to effectively apply model-based versus model-free learning approaches based on the problem at hand.
- Exploration-Exploitation Balance: Learn strategies for effectively balancing exploration of new possibilities and exploitation of known successful actions during the learning process.
- On-Policy and Off-Policy Learning: Gain expertise in learning from both on-policy data (data generated by the policy being evaluated) and off-policy data (data generated by a different policy).
Syllabus Highlights
The comprehensive syllabus covers a range of essential topics in reinforcement learning, providing a structured path to mastery:
- Multi-Armed Bandits: Foundational concepts of exploration and exploitation.
- Finite Markov Decision Processes: Formalizing sequential decision problems.
- Dynamic Programming: Classical methods for solving MDPs.
- Monte Carlo Methods: Learning from complete episodes of experience.
- Temporal-Difference Learning: Learning from incomplete episodes, including SARSA and Q-learning.
- n-step Bootstrapping: Bridging Monte Carlo and TD methods for efficient learning.
- Planning and Learning: Integrating model-based planning with reinforcement learning.
- On-Policy Prediction with Approximation: Scaling up prediction to large state spaces.
- On-Policy Control with Approximation: Developing practical control algorithms.
- Off-Policy Methods with Approximation: Learning from diverse data sources.
- Eligibility Traces: Efficiently assigning credit to past actions.
- Policy Gradient Methods: Directly optimizing policies in complex environments.
Leading this course are renowned experts in the field, Professor Peter Stone and Professor Scott Niekum. Both instructors are active researchers in reinforcement learning, bringing their cutting-edge knowledge and passion directly into the online classroom. Their expertise ensures that students receive not only a theoretical grounding but also practical insights into the latest advancements and research directions in RL.
Peter Stone Instructor of Record
Professor, Computer Science
Adjunct Assistant Professor, Computer Science
An Online Ms Cs with a specialization or strong focus on reinforcement learning is an ideal pathway for individuals aiming to contribute to the AI revolution. The skills and knowledge gained from such a program are highly sought after in various industries, including robotics, automation, game development, finance, and more. By choosing an online format, students gain the flexibility to balance their studies with professional and personal commitments, making advanced education accessible to a wider audience. Embark on your journey to becoming an AI expert with an online MS CS and unlock the potential of reinforcement learning.
