Deep Reinforcement Learning

10/15/2018
by   Yuxi Li, et al.
0

We discuss deep reinforcement learning in an overview style. We draw a big picture, filled with details. We discuss six core elements, six important mechanisms, and twelve applications, focusing on contemporary work, and in historical contexts. We start with background of artificial intelligence, machine learning, deep learning, and reinforcement learning (RL), with resources. Next we discuss RL core elements, including value function, policy, reward, model, exploration vs. exploitation, and representation. Then we discuss important mechanisms for RL, including attention and memory, unsupervised learning, hierarchical RL, multi-agent RL, relational RL, and learning to learn. After that, we discuss RL applications, including games, robotics, natural language processing (NLP), computer vision, finance, business management, healthcare, education, energy, transportation, computer systems, and, science, engineering, and art. Finally we summarize briefly, discuss challenges and opportunities, and close with an epilogue.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/19/2019

Reinforcement Learning Applications

We start with a brief introduction to reinforcement learning (RL), about...
research
02/23/2022

Deep Reinforcement Learning: Opportunities and Challenges

This article is a gentle discussion about the field of reinforcement lea...
research
07/12/2023

Transformers in Reinforcement Learning: A Survey

Transformers have significantly impacted domains like natural language p...
research
07/31/2023

Reinforcement Learning for Generative AI: State of the Art, Opportunities and Open Research Challenges

Generative Artificial Intelligence (AI) is one of the most exciting deve...
research
08/28/2019

Reinforcement Learning: Prediction, Control and Value Function Approximation

With the increasing power of computers and the rapid development of self...
research
03/31/2023

Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization

This paper presents a review of the field of reinforcement learning (RL)...
research
07/15/2021

Reinforcement Learning for Education: Opportunities and Challenges

This survey article has grown out of the RL4ED workshop organized by the...

Please sign up or login with your details

Forgot password? Click here to reset