Multi-agent reinforcement learning

Introduction to Multi-Agent Reinforcement Learning

Learn what multi-agent reinforcement learning is and some of the challenges it faces and overcomes. You will also learn what an agent is and how multi-agent systems can be both cooperative and adversarial. Be walked through a grid world example to highlight some of the benefits of both de

From playlist Reinforcement Learning

Multi Type Mean Field Reinforcement Learning | AISC

For slides and more information on the paper, visit https://ai.science/e/multi-type-mean-field-reinforcement-learning--ZPQxNPfeGM02aiyTqViE Discussion lead: Sriram Ganapathi Subramanian, Matthew Taylor This paper presents scaling up RL to hundreds or thousands of agents using a "mean fie

From playlist Reinforcement Learning

Deep Reinforcement Learning: Neural Networks for Learning Control Laws

Deep learning is enabling tremendous breakthroughs in the power of reinforcement learning for control. From games, like chess and alpha Go, to robotic systems, deep neural networks are providing a powerful and flexible representation framework that fits naturally with reinforcement learni

From playlist Reinforcement Learning

Q Learning Intro/Table - Reinforcement Learning p.1

Welcome to a reinforcement learning tutorial. In this part, we're going to focus on Q-Learning. Q-Learning is a model-free form of machine learning, in the sense that the AI "agent" does not need to know or have a model of the environment that it will be in. The same algorithm can be used

From playlist Reinforcement Learning

Reinforcement Learning Policies and Learning Algorithms

This video provides an introduction to the algorithms that reside within the agent. We’ll cover why we use neural networks to represent functions and why you may have to set up two neural networks in a powerful family of methods called actor-critic. Watch our full video series about Reinf

From playlist Reinforcement Learning

Q-Learning: Model Free Reinforcement Learning and Temporal Difference Learning

Here we describe Q-learning, which is one of the most popular methods in reinforcement learning. Q-learning is a type of temporal difference learning. We discuss other TD algorithms, such as SARSA, and connections to biological learning through dopamine. Q-learning is also one of the mo

From playlist Reinforcement Learning

Understanding Reinforcement Learning Environment and Rewards

In this video, we build on our basic understanding of reinforcement learning by exploring the workflow. We cover what an environment is and some of the benefits of training within a simulated environment. We cover what we ultimately want our agent to do and how crafting a reward function

From playlist Reinforcement Learning

Reinforcement Learning Series: Overview of Methods

This video introduces the variety of methods for model-based and model-free reinforcement learning, including: dynamic programming, value and policy iteration, Q-learning, deep RL, TD-learning, SARSA, policy gradient optimization, among others. This is the overview in a series on reinfo

From playlist Reinforcement Learning

Multicore Deep Reinforcement Learning | Asynchronous Advantage Actor Critic (A3C) Tutorial (PYTORCH)

Asynchronous advantage actor critic methods are a particular variant of asynchronous deep reinforcement learning that takes a totally different approach to breaking correlations in the data we feed to our deep neural network. Instead of using a replay buffer, we are going to use many ind

From playlist Deep Reinforcement Learning Tutorials - All Videos

AMMI Course "Geometric Deep Learning" - Seminar 1 (GDL & Reinforcement Learning) - Elise van der Pol

Video recording of the course "Geometric Deep Learning" taught in the African Master in Machine Intelligence in July-August 2021 by Michael Bronstein (Imperial College/Twitter), Joan Bruna (NYU), Taco Cohen (Qualcomm), and Petar Veličković (DeepMind) Seminar 1: Geometric Deep Learning and

From playlist AMMI Geometric Deep Learning Course - First Edition (2021)

Reinforcement Learning: Machine Learning Meets Control Theory

Reinforcement learning is a powerful technique at the intersection of machine learning and control theory, and it is inspired by how biological systems learn to interact with their environment. In this video, we provide a high level overview of reinforcement learning, along with leading a

From playlist Reinforcement Learning

Can AI Learn to Cooperate? Multi Agent Deep Deterministic Policy Gradients (MADDPG) in PyTorch

Multi agent deep deterministic policy gradients is one of the first successful algorithms for multi agent artificial intelligence. Cooperation and competition among AI agents is going to be critical as applications of deep learning expand in our daily lives. In this tutorial, we are going

From playlist Advanced Actor Critic and Policy Gradient Methods

AlphaStar: Grandmaster level in StarCraft II using multi-agent reinforcement learning

DeepMind's new agent to tackle yet another Esport: Starcraft II. This agent uses deep reinforcement learning with a new technique, called League Training, to catapult itself to Grandmaster-level skill at playing this game. Abstract: Many real-world applications require artificial agents t

From playlist Reinforcement Learning

Training a Reinforcement Learning Agent to Play Soccer

Check out Miguel Morales' book 📖 Grokking Deep Reinforcement Learning | http://mng.bz/K2mj 📖 To save 40% off this book ⭐ DISCOUNT CODE: twitmora40 ⭐ "The Agent Whisperer" goes over a reinforcement learning multi-agent (soccer) environment released by Google Research and demonstrates how it

From playlist Machine Learning

The Role of Multi-Agent Learning in Artificial Intelligence Research at DeepMind

Event Blurb: In computer science, an agent can be thought of as a computational entity that repeatedly perceives the environment, and takes action so as to optimize long term reward. We consider intelligence to be the ability of an agent to achieve goals in a wide range of environments (

From playlist AI at the Turing

Natasha Jaques - Social Reinforcement Learning - IPAM at UCLA

Recorded 19 February 2022. Natasha Jaques of Google AI presents "Social Reinforcement Learning" at IPAM's Mathematics of Collective Intelligence Workshop. Learn more online at: http://www.ipam.ucla.edu/programs/workshops/mathematics-of-intelligences/?tab=schedule

From playlist Workshop: Mathematics of Collective Intelligence - Feb. 15 - 19, 2022.

AI Weekly Update #6 September 22nd, 2019

0:40 OpenAI Multi-Agent Hide-and-Seek 6:41 OpenAI Fine-Tuning GPT-2 with RL 8:57 Facebook Wav2Vec 11:26 Facebook Self-Supervised QA dataset generation 12:40 Facebook at Interspeech 2019 13:34 Facebook Fashion++ 14:54 Google at Interspeech 2019 15:46 Sebastian Ruder Monthly NLP Newsletter 1

From playlist AI Research Weekly Updates

Reinforcement Learning: Deep Q Learning and Policy Gradient

From playlist Machine Learning