Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.
Covers the basics of reinforcement learning, including Markov Decision Processes and policy gradient methods, and explores real-world applications and recent advances.
Covers MuZero, a model that learns to predict rewards and actions iteratively, achieving state-of-the-art performance in board games and Atari video games.
Explores bug-finding, verification, and the use of learning-aided approaches in program reasoning, showcasing examples like the Heartbleed bug and differential Bayesian reasoning.
Discusses advanced reinforcement learning techniques, focusing on deep and robust methods, including actor-critic frameworks and adversarial learning strategies.
Covers the significance of subtracting the mean reward in policy gradient methods for deep reinforcement learning, reducing noise in the stochastic gradient.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
