Learning Controllers - From Engineering to AGI
Exploring deep reinforcement learning for autonomous control systems, from fusion plasma to robotics, with a focus on data-efficient methods for real-world applications.
Tractable Novelty Exploration Over Continuous and Discrete Sequential Decision Problems
Explore cutting-edge width-based planning algorithms for sequential decision problems, covering continuous dynamics, polynomial approximations of novelty, and comparisons with deep reinforcement learning.
Reinforcement Learning Course
Comprehensive exploration of reinforcement learning concepts, from basics to advanced topics, including deep RL, policy gradients, and practical applications in classic games.
Deep Reinforcement Learning for Object Tracking
Explore action-driven object tracking using deep reinforcement learning, covering policy, value functions, and training approaches for improved visual tracking performance.
Neural Networks
This Course is a friendly introduction series to machine learning, deep learning, neural networks and generative adversarial networks.
Reinforcement Learning
Explora el Aprendizaje por Refuerzo, desde métodos básicos hasta redes neuronales y aplicaciones en dinámica de fluidos y control, en esta serie de 3.5 horas por Steve Brunton.
Data-Driven Control with Machine Learning
Explore machine learning techniques for data-driven control, covering system identification, model reduction, and advanced control strategies like MPC and reinforcement learning.
TF-Agents - A Flexible Reinforcement Learning Library for TensorFlow
Explore TF-Agents, a flexible Deep Reinforcement Learning library for TensorFlow. Learn about recent advancements, implementation techniques, and how to jumpstart your RL projects using this modular open-source tool.
TensorFlow and Deep Reinforcement Learning, Without a PhD - Google I/O '18
Learn to implement deep reinforcement learning with TensorFlow, teaching a neural network to play Pong using only pixel data. No prior expertise required.
MIT: Deep Reinforcement Learning
Explore deep reinforcement learning concepts, algorithms, and applications, including Q-learning, policy gradients, and breakthroughs like AlphaGo and AlphaZero in game AI.
Deep Reinforcement Learning in the Real World - Sergey Levine
Explore deep reinforcement learning applications in real-world scenarios, focusing on off-policy methods, model-free and model-based approaches, and their practical implementations in robotics and manipulation tasks.
Machine Learning for Computing Tensor Rank
Explorez l'utilisation de l'apprentissage par renforcement pour découvrir de nouveaux algorithmes de multiplication matricielle et calculer le rang des tenseurs.
Optimization-in-the-Loop AI for Energy and Climate - IPAM at UCLA
Optimization-in-loop AI framework for climate action: enhancing ML models with physics, constraints, and decision-making processes for energy grids, building efficiency, and more.
Learning Linear Dynamical Systems with Hankel Nuclear Norm Regularization
Explore finite-sample analysis and insights for learning low-order linear dynamical systems using Hankel nuclear norm regularization, with applications in control and reinforcement learning.
Open Source Summit and Embedded Linux Conference Keynotes - Day 3
Explore cutting-edge open-source technologies through keynotes on cloud-native projects, safety-critical applications, infrastructure modernization, and RISC-V, featuring industry experts and innovative case studies.
Developing Robots that Autonomously Learn and Plan in the Real World
Explore autonomous robot learning and planning, covering challenges, techniques, and future directions in creating versatile AI agents capable of solving complex real-world tasks.
Preserving Patient Safety as AI Transforms Clinical Care - Curt Langlotz, Stanford University
Explore AI's impact on healthcare, focusing on patient safety, data privacy, and interpretability. Learn about new machine learning methods supporting caregivers and enhancing clinical decision-making.
Neural Network Verification as Piecewise Linear Optimization
Neural network verification as piecewise linear optimization: exploring robust methods to prove model reliability against attacks, using LP and MIP formulations for efficient problem-solving in various network types.
Modelling and Controlling Turbulent Flows Through Deep Learning
Explore deep learning applications in fluid mechanics, including flow prediction and control using CNNs, autoencoders, and GANs. Learn about non-intrusive sensing and transfer learning in turbulent flows.
Deep Learning: Deep Reinforcement Learning and Policy Gradients - Lecture 13
Dive into advanced deep reinforcement learning concepts including policy gradients, REINFORCE algorithm, and their applications in Atari games and Go, taught by Ali Ghodsi.
Challenges in Reward Design for Reinforcement Learning-based Traffic Signal Control - An Investigation Using CO2 Emission Objective
Explore the complexities of reward design in reinforcement learning for traffic signal control, focusing on CO2 emission reduction through Deep Q-Network implementation and reward optimization strategies.