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Teaching KNIME to Play Tic-Tac-Toe
In this blog post I want to introduce some basic concepts of reinforcement learning, some important terminology, and show a simple use case where I create a game playing AI in KNIME Analytics Platform. After reading this, I hope you’ll have a better understanding of the... Read more
How to Make Sense of the Reinforcement Learning Agents? What and Why I Log During Training and Debug
Based on simply watching how an agent acts in the environment, it is hard to tell anything about why it behaves this way and how it works internally. That’s why it is crucial to establish metrics that tell WHY the agent performs in a certain way.... Read more
Reinforcement Learning with Ray RLlib
Why Reinforcement Learning? In reinforcement learning (RL), an agent tries to maximize a reward while interacting with an environment. The agent observes the state of the environment, takes an action and observes the reward received (if any) and the new state. Then the agent takes the... Read more
Explore Fundamental Concepts of Reinforcement Learning
Imagine that you want to learn to ride a bike and ask a friend for advice. They explain how the gears work, how to release the brake and a few other technical details. In the end, you ask the secret to keeping your balance. What kind... Read more
Deep Q-Learning Algorithm in Reinforcement Learning
In this article, we will discuss Q-learning in conjunction with neural networks (NNs). This combination has the name deep Q-network (DQN). This article is an excerpt from the book Deep Reinforcement Learning Hands-on, Second Edition by Max Lapan. This book provides you with an introduction to the... Read more
Best Deep Reinforcement Learning Research of 2019
Since my mid-2019 report on the state of deep reinforcement learning (DRL) research, much has happened to accelerate the field further. Read my previous article for a bit of background, brief overview of the technology, comprehensive survey paper reference, along with some of the best research... Read more
What You Need to Know about DeepMind’s BSuite
Imagine this. You know that reinforcement learning has been responsible for some of AI’s most significant advancements. You’re in the exploratory phase of implementing your first project. You’d love a way to evaluate whether your RL agent is appropriate for the task you have, something not... Read more
Behavior Suite for Reinforcement Learning
A team from DeepMind Technologies—made up of Ian Osband, Yotam Doron, Matteo Hessel, John Aslanides, Eren Sezner, Andre Saraiva, Katrina McKinney, Tor Lattimore, Csaba Szepezvari, Satinder Singh, Benjamin Van Roy, Richard Sutton, David Silver, and Hado Van Hesselt—has recently published a piece on their new program... Read more
Best Deep Reinforcement Learning Research of 2019 So Far
In this article, I’ve conducted an informal survey of all the deep reinforcement learning research thus far in 2019 and I’ve picked out some of my favorite papers. This list should make for some enjoyable summer reading!   [Related Article: 10 Compelling Machine Learning Dissertations from Ph.D.... Read more
Watch: Imitation Learning: Reinforcement Learning For The Real World
This talk will introduce the formal Imitation Learning problem and discuss two main categories of agent training in the Imitation Learning paradigm: behavioral cloning and interactive experts. The talk will also include examples of where it could be used to solve real-world problems and a demonstration... Read more