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Related lectures (21)

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.

Monte Carlo Tree Search and Alpha Zero

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

Subtracting the mean reward via the value function

Covers the significance of subtracting the mean reward in policy gradient methods for deep reinforcement learning, reducing noise in the stochastic gradient.

Model-Based Deep RL: Planning and VAST

Covers model-based reinforcement learning, planning, variational state tabulation, and efficient Q- and V-values updating.

Reinforcement Learning: BackUp Diagrams

Introduces the BackUp diagram as a key graphic representation in reinforcement learning.

Proximal Policy Optimization for Continuous Control

Explores Proximal Policy Optimization for enhancing stability and efficiency in continuous control with deep reinforcement learning.

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.

Mini-Batches in On- and Off-Policy Deep Reinforcement Learning

Explains the significance of mini-batches in Deep Reinforcement Learning and the differences between on-policy and off-policy methods.

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.

Deep Reinforcement Learning: Proximal Policy Optimization Techniques

Covers deep reinforcement learning techniques for continuous control, focusing on proximal policy optimization methods and their advantages over standard policy gradient approaches.

Reinforcement Learning: TD Learning and SARSA Variants

Discusses reinforcement learning, focusing on temporal difference learning and SARSA algorithm variations.

Policy Gradient Methods: Direct Action Learning in Reinforcement Learning

Covers policy gradient methods, focusing on direct action learning and optimizing rewards in reinforcement learning.

Deep Q-Learning: DeepRL1.1

Covers Deep Q-learning in deep neural networks, its application in games, backpropagation, Q-values, and V-values.

Vision-Based Quadrotor Navigation Log in to Mediaspace to watch this video

Discusses quadrotor navigation using deep reinforcement learning and low-level control, focusing on visual intelligence and gaze model robustness.

Deep Learning Agents: Reinforcement Learning Log in to Mediaspace to watch this video

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

Reinforcement Learning: Basics and Applications Log in to Mediaspace to watch this video

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

Learning-aided Program Reasoning Log in to Mediaspace to watch this video

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

Introduction to Data Science Log in to Mediaspace to watch this video

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

Deep and Robust Reinforcement Learning Techniques Log in to Mediaspace to watch this video

Discusses advanced reinforcement learning techniques, focusing on deep and robust methods, including actor-critic frameworks and adversarial learning strategies.

Reinforcement Learning: Basics and Applications Log in to Mediaspace to watch this video

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