Learning a Belief Representation for Delayed Reinforcement Learning

2021 ◽  
Author(s):  
Pierre Liotet ◽  
Erick Venneri ◽  
Marcello Restelli
Keyword(s):  
Reinforcement Learning ◽  
Delayed Reinforcement

Delayed reinforcement learning

2004 ◽  
Author(s):  
S Sathiya Keerthi ◽  
B Ravindran
Keyword(s):  
Reinforcement Learning ◽  
Delayed Reinforcement

DEEP-SARSA: A REINFORCEMENT LEARNING ALGORITHM FOR AUTONOMOUS NAVIGATION

2001 ◽  
Vol 12 (10) ◽  
pp. 1513-1523 ◽  
Author(s):  
M. ANDRECUT ◽  
M. K. ALI
Keyword(s):  
Reinforcement Learning ◽  
Autonomous Navigation ◽  
Learning Algorithm ◽  
Robot Navigation ◽  
Learning Algorithms ◽  
Delayed Reinforcement ◽  
Graph Searching ◽  
Ill Posed ◽  
Autonomous Robot Navigation ◽  
Reinforcement Learning Algorithm

In this paper we discuss the application of reinforcement learning algorithms to the problem of autonomous robot navigation. We show that the autonomous navigation using the standard delayed reinforcement learning algorithms is an ill posed problem and we present a more efficient algorithm for which the convergence speed is greatly improved. The proposed algorithm (Deep-Sarsa) is based on a combination between the Depth-First Search (a graph searching algorithm) and Sarsa (a delayed reinforcement learning algorithm).

scholarly journals Reinforcement Learning: A Survey

1996 ◽  
Vol 4 ◽  
pp. 237-285 ◽  
Author(s):  
L. P. Kaelbling ◽  
M. L. Littman ◽  
A. W. Moore
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Environment ◽  
Delayed Reinforcement ◽  
Trial And Error ◽  
Exploration And Exploitation ◽  
Markov Decision Theory ◽  
Markov Decision ◽  
Historical Basis ◽  
And Coping ◽  
Selection Of

This paper surveys the field of reinforcement learning from a computer-science perspective. It is written to be accessible to researchers familiar with machine learning. Both the historical basis of the field and a broad selection of current work are summarized. Reinforcement learning is the problem faced by an agent that learns behavior through trial-and-error interactions with a dynamic environment. The work described here has a resemblance to work in psychology, but differs considerably in the details and in the use of the word ``reinforcement.'' The paper discusses central issues of reinforcement learning, including trading off exploration and exploitation, establishing the foundations of the field via Markov decision theory, learning from delayed reinforcement, constructing empirical models to accelerate learning, making use of generalization and hierarchy, and coping with hidden state. It concludes with a survey of some implemented systems and an assessment of the practical utility of current methods for reinforcement learning.

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models
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