Discounted Reinforcement Learning is Not an Optimization Problem

arXiv.org Artificial Intelligence

Discounted reinforcement learning is fundamentally incom patible with function approximation for control in continuing tasks. This is beca use it is not an optimization problem -- it lacks an objective function. After s ubstantiating these claims, we go on to address some misconceptions about discou nting and its connection to the average reward formulation. W e encourage res earchers to adopt rigorous optimization approaches for reinforcement learn ing in continuing tasks, such as average reward.


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

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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.


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

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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.


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).