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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 next action, and... 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 of answer do... 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 fundamentals of RL,... 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 papers at that... 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 always apparent without... 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 Behavior Suite (bsuite... 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!   As we... 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 of the DAgger... Read more
Bears Need to Learn as well – Practical Reinforcement Learning with TensorFlow 2.0 & TF-Agents
Editor’s Note: Oliver is speaking at ODSC West 2019, see his talk “Reinforcement Learning with TF Agents & TensorFlow 2.0: Hands On” there. Have a look at our friend Orso the bear.  Orso lives in his cave and knows his area and where he can typically find some honey.... Read more
Watch: Introduction to Reinforcement Learning
Reinforcement Learning (RL), the field of sequential decision making, has evolved significantly within the last few years, achieving super-human performance in solving complex board games, 2D Atari and 3D games (Doom, Quake, StarCraft). But this is not just about games, this is about solving arbitrary problems with truly general... Read more