Deep Learning -- Reinforcement Learning – Data Driven Investor – Medium

#artificialintelligence

Interested in understanding the algorithm used by AlphaGo to beat the human world champion? Then this article is for you. We will discuss what is Reinforcement learning (RL), Elements of Reinforced Learning, terms related to RL like value function, and Q value function. As kids, teenagers or grownups, when we we need to learn a new skill, we either have someone to help or we learn on our own by trial and error. Let's map this to Reinforced Learning.


Reinforcement Learning in R

arXiv.org Machine Learning

Reinforcement learning refers to a group of methods from artificial intelligence where an agent performs learning through trial and error. It differs from supervised learning, since reinforcement learning requires no explicit labels; instead, the agent interacts continuously with its environment. That is, the agent starts in a specific state and then performs an action, based on which it transitions to a new state and, depending on the outcome, receives a reward. Different strategies (e.g. Q-learning) have been proposed to maximize the overall reward, resulting in a so-called policy, which defines the best possible action in each state. Mathematically, this process can be formalized by a Markov decision process and it has been implemented by packages in R; however, there is currently no package available for reinforcement learning. As a remedy, this paper demonstrates how to perform reinforcement learning in R and, for this purpose, introduces the ReinforcementLearning package. The package provides a remarkably flexible framework and is easily applied to a wide range of different problems. We demonstrate its use by drawing upon common examples from the literature (e.g. finding optimal game strategies).


A Beginner's Guide to Deep Reinforcement Learning (for Java and Scala) - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM

@machinelearnbot

While neural networks are responsible for recent breakthroughs in problems like computer vision, machine translation and time series prediction – they can also combine with reinforcement learning algorithms to create something astounding like AlphaGo.


A Beginner's Guide to Deep Reinforcement Learning (for Java and Scala) - Deeplearning4j: Open-source, Distributed Deep Learning for the JVM

#artificialintelligence

While neural networks are responsible for recent breakthroughs in problems like computer vision, machine translation and time series prediction – they can also combine with reinforcement learning algorithms to create something astounding like AlphaGo.


Policy Gradients in a Nutshell – Towards Data Science

#artificialintelligence

Reinforcement Learning (RL) refers to both the learning problem and the sub-field of machine learning which has lately been in the news for great reasons. RL based systems have now beaten world champions of Go, helped operate datacenters better and mastered a wide variety of Atari games. The research community is seeing many more promising results. With enough motivation, let us now take a look at the Reinforcement Learning problem. Reinforcement Learning is the most general description of the learning problem where the aim is to maximize a long-term objective.