scholarly journals Prioritized sweeping: Reinforcement learning with less data and less time

1993 ◽  
Vol 13 (1) ◽  
pp. 103-130 ◽  
Author(s):  
Andrew W. Moore ◽  
Christopher G. Atkeson
Keyword(s):  
Reinforcement Learning ◽  
Prioritized Sweeping

Prioritized Sweeping Reinforcement Learning Based Routing for MANETs

2017 ◽  
Vol 5 (2) ◽  
pp. 383 ◽  
Author(s):  
Rahul M Desai ◽  
B P Patil
Keyword(s):  
Reinforcement Learning ◽  
Shortest Path ◽  
Ad Hoc ◽  
Mobile Ad Hoc Network ◽  
Network Routing ◽  
Delivery Ratio ◽  
Learning Method ◽  
Shortest Path Routing ◽  
Mobile Ad Hoc ◽  
Prioritized Sweeping

<p class="Default">In this paper, prioritized sweeping confidence based dual reinforcement learning based adaptive network routing is investigated. Shortest Path routing is always not suitable for any wireless mobile network as in high traffic conditions, shortest path will always select the shortest path which is in terms of number of hops, between source and destination thus generating more congestion. In prioritized sweeping reinforcement learning method, optimization is carried out over confidence based dual reinforcement routing on mobile ad hoc network and path is selected based on the actual traffic present on the network at real time. Thus they guarantee the least delivery time to reach the packets to the destination. Analysis is done on 50 Nodes Mobile ad hoc networks with random mobility. Various performance parameters such as Interval and number of nodes are used for judging the network. Packet delivery ratio, dropping ratio and delay shows optimum results using the prioritized sweeping reinforcement learning method.</p>

scholarly journals Accelerating Reinforcement Learning through Implicit Imitation

2003 ◽  
Vol 19 ◽  
pp. 569-629 ◽  
Author(s):  
B. Price ◽  
C. Boutilier
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Formal Model ◽  
The State ◽  
Learning Agent ◽  
Relative Value ◽  
Multiagent Environments ◽  
Improved Performance ◽  
Prioritized Sweeping ◽  
Extract Information

Imitation can be viewed as a means of enhancing learning in multiagent environments. It augments an agent's ability to learn useful behaviors by making intelligent use of the knowledge implicit in behaviors demonstrated by cooperative teachers or other more experienced agents. We propose and study a formal model of implicit imitation that can accelerate reinforcement learning dramatically in certain cases. Roughly, by observing a mentor, a reinforcement-learning agent can extract information about its own capabilities in, and the relative value of, unvisited parts of the state space. We study two specific instantiations of this model, one in which the learning agent and the mentor have identical abilities, and one designed to deal with agents and mentors with different action sets. We illustrate the benefits of implicit imitation by integrating it with prioritized sweeping, and demonstrating improved performance and convergence through observation of single and multiple mentors. Though we make some stringent assumptions regarding observability and possible interactions, we briefly comment on extensions of the model that relax these restricitions.

Incremental Reinforcement Learning With Prioritized Sweeping for Dynamic Environments

2019 ◽  
Vol 24 (2) ◽  
pp. 621-632 ◽  
Author(s):  
Zhi Wang ◽  
Chunlin Chen ◽  
Han-Xiong Li ◽  
Daoyi Dong ◽  
Tzyh-Jong Tarn
Keyword(s):  
Reinforcement Learning ◽  
Dynamic Environments ◽  
Prioritized Sweeping

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

Analogical Reinforcement Learning With Two-Stage Memory Retrieval

2014 ◽  
Author(s):  
James Foster ◽  
Matt Jones
Keyword(s):  
Reinforcement Learning ◽  
Memory Retrieval ◽  
Two Stage
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