SmartIX: A database indexing agent based on reinforcement learning

2020 ◽  
Vol 50 (8) ◽  
pp. 2575-2588 ◽  
Author(s):  
Gabriel Paludo Licks ◽  
Julia Colleoni Couto ◽  
Priscilla de Fátima Miehe ◽  
Renata de Paris ◽  
Duncan Dubugras Ruiz ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Agent Based ◽  
Database Indexing

scholarly journals A strategy learning model for autonomous agents based on classification

2015 ◽  
Vol 25 (3) ◽  
pp. 471-482 ◽  
Author(s):  
Bartłomiej Śnieżyński
Keyword(s):  
Reinforcement Learning ◽  
Supervised Learning ◽  
Learning Process ◽  
Autonomous Agents ◽  
Good Alternative ◽  
Learning Model ◽  
Learning Method ◽  
Complex Environments ◽  
Agent Based ◽  
Proposed Model

AbstractIn this paper we propose a strategy learning model for autonomous agents based on classification. In the literature, the most commonly used learning method in agent-based systems is reinforcement learning. In our opinion, classification can be considered a good alternative. This type of supervised learning can be used to generate a classifier that allows the agent to choose an appropriate action for execution. Experimental results show that this model can be successfully applied for strategy generation even if rewards are delayed. We compare the efficiency of the proposed model and reinforcement learning using the farmer-pest domain and configurations of various complexity. In complex environments, supervised learning can improve the performance of agents much faster that reinforcement learning. If an appropriate knowledge representation is used, the learned knowledge may be analyzed by humans, which allows tracking the learning process

Behavior Selection Using Utility-Based Reinforcement Learning in Irregular Warfare Simulation Models

2013 ◽  
Vol 4 (3) ◽  
pp. 61-78 ◽  
Author(s):  
Sotiris Papadopoulos ◽  
Francisco Baez ◽  
Jonathan Alt ◽  
Christian Darken
Keyword(s):  
Reinforcement Learning ◽  
Adaptive Learning ◽  
Simulation Models ◽  
The Body ◽  
Irregular Warfare ◽  
Agent Based ◽  
Body Of Knowledge ◽  
Learning Techniques ◽  
Study Results ◽  
The Impact

The Theory of Planned Behavior (TPB) provides a conceptual model for use in assessing behavioral intentions of humans. Agent based social simulations seek to represent the behavior of individuals in societies in order to understand the impact of a variety of interventions on the population in a given area. Previous work has described the implementation of the TPB in agent based social simulation using Bayesian networks. This paper describes the implementation of the TPB using novel learning techniques related to reinforcement learning. This paper provides case study results from an agent based simulation for behavior related to commodity consumption. Initial results demonstrate behavior more closely related to observable human behavior. This work contributes to the body of knowledge on adaptive learning behavior in agent based simulations.

scholarly journals Distributed MPC for Large Scale Systems using Agent-based Reinforcement Learning

2010 ◽  
Vol 43 (8) ◽  
pp. 597-602 ◽  
Author(s):  
Valeria Javalera ◽  
Bernardo Morcego ◽  
Vicenç Puig
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Agent Based ◽  
Large Scale Systems
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