Deep reinforcement learning

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Related lectures (29)

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Advanced Machine Learning: Fundamentals and Applications

Covers the fundamentals of advanced machine learning, emphasizing practical applications through interactive exercises and projects.

Reinforcement Learning: Q-Learning

Introduces Q-Learning, Deep Q-Learning, REINFORCE algorithm, and Monte-Carlo Tree Search in reinforcement learning, culminating in AlphaGo Zero.

Neuroscience and AI: Bridging the Gap

Explores the gap between AI and human intelligence through neuroscience-inspired models and algorithms.

Rethinking robot learning for autonomy at scale

Explores advancements in robot learning for autonomy at scale, covering deep learning challenges, efficient architecture, benchmarking results, and societal implications.

Reinforcement Learning: Basics and Applications

Covers the basics of reinforcement learning, including trial-and-error learning, Q-learning, deep RL, and applications in gaming and planning.

Alpha Go: Machine Learning and Game Strategies

Explores the historical perspective and development of the AlphaGo algorithm, focusing on machine learning and game strategies.

Reinforcement Learning: Basics

Covers the basics of reinforcement learning, including Q-learning and neural networks.

Deep Reinforcement Learning: Mini-Batches and Policy Methods

Discusses deep reinforcement learning methods, focusing on mini-batches and the implications of on-policy and off-policy training techniques.

Bullet Arm: Robotic Manipulation Benchmark

Introduces BulletArm, an open-source robotic manipulation benchmark and learning framework, covering design goals, benchmark tasks, and learning algorithms.