Lecture

Monte Carlo Tree Search and Alpha Zero

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Related lectures (64)

Collective Learning Dynamics: Similarity Exploitation

Delves into collective learning dynamics with similarity exploitation, covering structured learning, adaptive frameworks, modeling, simulation, and experimental results.

Dynamics of Linear Neural Networks

Explores the learning dynamics of deep neural networks using linear networks for analysis, covering two-layer and multi-layer networks, self-supervised learning, and benefits of decoupled initialization.

Reinforcement Learning: Q-Learning

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

Deep Learning: Data Representations and Neural Networks

Explores data representations, histograms, neural networks, and deep learning concepts.

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.

Autonomous Vehicles: Intelligence and Perception

Explores intelligence, perception, and AI applications in autonomous vehicles, emphasizing rational thinking and social intelligence.

Deep Learning Fundamentals

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Machine Learning Fundamentals

Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.

Neural Networks: Multi-layers

Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.

Backpropagation and Neural Networks

Covers the backpropagation algorithm for training neural networks and the representation of functions in multilayer networks.

Policy Gradient Methods: Direct Action Learning in Reinforcement Learning

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

Multilayer Networks: First Steps

Covers the preparation for deriving the Backprop algorithm in layered networks using multi-layer perceptrons and gradient descent.

Image Processing II: Neural Networks and CNNs

Explores the evolution of CNNs in image processing, covering classical and deep neural networks, training algorithms, backpropagation, non-linear steps, loss functions, and software frameworks.

Value Iteration Acceleration: PID and Operator Splitting

Explores accelerating the Value Iteration algorithm using control theory and matrix splitting techniques to achieve faster convergence.

Learning Agents: Exploration-Exploitation Tradeoff

Explores the exploration-exploitation tradeoff in learning unknown effects of actions using multi-armed bandits and Q-learning.

Non conceptual knowledge systems

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.

Proximal Policy Optimization for Continuous Control

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

Deep Learning: Data Representation and Multilayer Perceptron

Covers data representation, MLP training, activation functions, and gradient-based learning in deep neural networks.

Self-supervised Learning for Autonomous Vehicles

Explores self-supervised learning for autonomous vehicles, deriving labels from data itself and discussing its applications and challenges.

Deep Learning: Fundamentals

Covers the fundamentals of deep learning, from artificial neurons to modern networks.