<h2>About this course</h2>
<p>This lecture series brings together world-class <a href="/for/researchers">researchers</a> from DeepMind and University College London to teach <a href="/topics/reinforcement-learning">reinforcement learning</a> from first principles. You'll learn from pioneers like Hado van Hasselt and David Silver—researchers who've shaped how machines learn to make decisions in complex environments. Whether you're curious about game-playing AI, robotics, or autonomous systems, this course grounds you in the theory and practice that powers real-world AI applications.</p>

<h2>What you'll learn</h2>
<ul>
<li>Core principles of Markov decision processes and how to model sequential decision-making problems</li>
<li>Value functions and policy gradients—the two fundamental approaches to solving RL problems</li>
<li>Deep reinforcement learning techniques that combine <a href="/topics/neural-networks">neural networks</a> with RL algorithms</li>
<li>Exploration vs. exploitation trade-offs and how to balance discovering new strategies with using proven ones</li>
<li>Practical applications: game AI, robot control, and real-world decision-making systems</li>
<li>How to read and understand cutting-edge RL research papers from DeepMind and similar labs</li>
<li>Implementation patterns used in state-of-the-art RL systems (AlphaGo, MuZero, and beyond)</li>
</ul>

<h2>Who this is for</h2>
<p>This course is built for learners with a solid foundation in math and programming who want to move beyond basics. If you understand neural networks and are comfortable with linear algebra, you're ready to tackle reinforcement learning's depth.</p>
<ul>
<li>AI researchers and PhD <a href="/for/students">students</a> — deepen your theoretical understanding and learn techniques directly from DeepMind's leaders</li>
<li>Machine learning engineers — master the RL methods needed for autonomous systems, robotics, and game AI projects</li>
<li><a href="/for/data-scientists">Data scientists</a> — expand your toolkit beyond supervised learning into sequential decision-making problems</li>
</ul>

<h2>Prerequisites</h2>
<p>You'll need solid experience with Python, neural networks, probability, and linear algebra. Familiarity with machine learning fundamentals (supervised learning, backpropagation) is essential. This is an <strong>advanced course</strong>—it assumes you're comfortable reading mathematical notation and implementing algorithms from research papers.</p>

<h2>Why this matters for Indian learners</h2>
<p>Reinforcement learning is no longer a niche research topic—it's becoming critical across Indian tech. Companies like Flipkart and Amazon are exploring RL for recommendation systems and supply chain optimization. Self-driving vehicle startups, robotics labs, and fintech firms building trading algorithms all need RL expertise. Learning directly from DeepMind's researchers puts you on par with talent at the world's top AI labs, opening doors to senior research roles and specialized positions that command premium salaries in India's fast-growing AI sector.</p>

<h2>Frequently asked questions</h2>
<h3>Is this course really free?</h3>
<p>Yes. All 18 hours of lectures are freely available on YouTube. There's no hidden paywall or premium tier.</p>

<h3>How long will it take to complete?</h3>
<p>The lectures total 18 hours, but RL is conceptually dense—expect to spend 30-40 hours total when you factor in reading papers, working through math, and experimenting with code. A realistic pace is 3-4 hours per week, finishing in 10-12 weeks.</p>

<h3>Will I get a certificate?</h3>
<p>No formal certificate is offered. This is a research course, not a credentialing program. The value lies in what you learn and can build with it.</p>

DeepMind's reinforcement learning lecture series.

DeepMind

DeepMind's collaboration with UCL — lectures by leading researchers including Hado van Hasselt and David Silver.

DeepMind x UCL Reinforcement Learning Lecture Series

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