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.
Neural Networks
This Course is a friendly introduction series to machine learning, deep learning, neural networks and generative adversarial networks.
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.
Chain-of-Thought Limitations and Google's SCoRe Method for Language Models
Discover how Google's SCoRe method revolutionizes language models' self-correction abilities through reinforcement learning, surpassing traditional chain-of-thought approaches in mathematical reasoning and code generation.
Average Reward Markov Decision Process - Policy Gradient Algorithms and Regret Analysis
Delve into infinite horizon average reward Markov Decision Process (MDP), exploring policy gradient algorithms, gradient estimation techniques, and innovative approaches to reduce mixing time dependencies.
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.
Learning Decentralized Policies in Multiagent Systems - How to Learn Efficiently
Explore decentralized policy learning in multiagent systems, focusing on scalable actor-critic methods, network structures, and performance characterization using stochastic game frameworks.
Neural Nets for NLP 2019 - Reinforcement Learning
Explore reinforcement learning in NLP, covering policy gradient, REINFORCE, value-based methods, and applications in dialogue and information retrieval.
Neural Nets for NLP - Minimum Risk Training and Reinforcement Learning
Explore minimum risk training and reinforcement learning in NLP, covering error analysis, policy gradients, and value-based methods to enhance neural network performance.
Trust Region Policy Optimization
Explore trust region policy optimization in reinforcement learning, covering key concepts like policy gradient, KL-penalized problems, and the TRPO algorithm.
Backpropagation and Deep Learning in the Brain
Explore deep learning in the brain, backpropagation, and credit assignment algorithms. Discover neuroscience evidence and new models of neurons in this computational brain theory talk.
Introduction to Reinforcement Learning
Explore reinforcement learning's evolution, breakthroughs, and applications in AI, from board games to robotics. Gain insights into key concepts, formulations, and future directions in this rapidly advancing field.
ADSI Summer Workshop- Algorithmic Foundations of Learning and Control
Explore model-based control for physical systems, covering inverse dynamics, optimization techniques, and automated discovery methods for advanced robotics and control applications.
A Friendly Introduction to Deep Reinforcement Learning, Q-Networks and Policy Gradients
Friendly exploration of deep reinforcement learning concepts, including Markov decision processes, Q-networks, and policy gradients, using examples and visuals to explain complex ideas.
MIT: Reinforcement Learning
Explore deep reinforcement learning concepts, algorithms, and real-world applications in this comprehensive lecture from MIT's Introduction to Deep Learning course.
Real-World AI: Risk and Robustness in Reinforcement Learning and Kalman Filtering
Explore real-world applications of AI through risk-aware reinforcement learning and robust Kalman filtering, focusing on practical challenges and solutions for handling uncertainties in AI systems.