Model-Based Deep RL: Planning and VAST

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Model-Based Deep Reinforcement Learning: Monte Carlo Tree Search

Explores model-based deep reinforcement learning, focusing on Monte Carlo Tree Search and its applications in game strategies and decision-making processes.

Monte Carlo Tree Search and Alpha Zero

Explores Monte Carlo Tree Search and Alpha Zero in deep reinforcement learning.

Deep Learning Agents: Reinforcement Learning

Explores Deep Learning Agents in Reinforcement Learning, emphasizing neural network approximations and challenges in training multiagent systems.

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.

Reinforcement Learning: Basics and Applications

Covers the basics of reinforcement learning, including Markov Decision Processes and policy gradient methods, and explores real-world applications and recent advances.

MuZero: Planning and Learning Model

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.

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.

Reinforcement Learning: Policy Gradient and Actor-Critic Methods

Provides an overview of reinforcement learning, focusing on policy gradient and actor-critic methods for deep artificial neural networks.

Introduction to Data Science

Introduces the basics of data science, covering decision trees, machine learning advancements, and deep reinforcement learning.

Learning-aided Program Reasoning

Explores bug-finding, verification, and the use of learning-aided approaches in program reasoning, showcasing examples like the Heartbleed bug and differential Bayesian reasoning.