Hierarchical State Representation and Action Abstractionsin Q-Learning for Agent-Based Herding

2012 ◽  
Author(s):  
Tao Mao
Keyword(s):  
State Representation ◽  
Agent Based ◽  
Q Learning

scholarly journals Intelligent motivation framework based on Q-network for multiple agents in Internet of Things simulations

2019 ◽  
Vol 15 (7) ◽  
pp. 155014771986638
Author(s):  
Hieu Trong Nguyen ◽  
Phuong Minh Chu ◽  
Jisun Park ◽  
Yunsick Sung ◽  
Kyungeun Cho
Keyword(s):  
Internet Of Things ◽  
Autonomous Vehicle ◽  
Three Dimensional ◽  
Virtual Agents ◽  
Daily Lives ◽  
Agent Based ◽  
Q Learning ◽  
Hierarchical Task Network ◽  
Average Accuracy ◽  
Motivation Model

Internet of Things simulations play significant roles in the diverse kinds of activities in our daily lives and have been extensively researched. Creating and controlling virtual agents in three-dimensional Internet of Things simulations is a key technology for achieving realism in three-dimensional simulations. Given that traditional virtual agent-based approaches have limitations for realism, it is necessary to improve the realism of three-dimensional Internet of Things simulations. This article proposes a Q-Network-based motivation framework that applies a Q-Network to select motivations from desires and hierarchical task network planning to execute actions based on goals of the selected motivations. The desires are to be identified and calculated based on states. Selected motivations will be chosen to determine the goals that agents must achieve. In the experiments, the proposed framework achieved an average accuracy of up to 85.5% when the Q-Network-based motivation model was trained. To verify the Q-Network-based motivation framework, a traditional Q-learning is also applied in the three-dimensional virtual environment. Comparing the results of the two frameworks, the Q-Network-based motivation framework shows better results than those of traditional Q-learning, as the accuracy of the Q-Network-based motivation is higher by 15.58%. The proposed framework can be applied to the diverse kinds of Internet of Things systems such as a training autonomous vehicle. Moreover, the proposed framework can generate big data on animal behaviors for other training systems.

Accelerating route choice learning with experience sharing in a commuting scenario: An agent-based approach

2020 ◽  
pp. 1-15
Author(s):  
Franziska Klügl ◽  
Ana Lucia C. Bazzan
Keyword(s):  
Reinforcement Learning ◽  
Route Choice ◽  
Learning Task ◽  
Individual Learning ◽  
Learning Dynamics ◽  
Agent Based ◽  
Q Learning ◽  
Traffic State ◽  
Aggregate Analysis ◽  
Interesting Pattern

Navigation apps have become more and more popular, as they give information about the current traffic state to drivers who then adapt their route choice. In commuting scenarios, where people repeatedly travel between a particular origin and destination, people tend to learn and adapt to different situations. What if the experience gained from such a learning task is shared via an app? In this paper, we analyse the effects that adaptive driver agents cause on the overall network, when those agents share their aggregated experience about route choice in a reinforcement learning setup. In particular, in this investigation, Q-learning is used and drivers share what they have learnt about the system, not just information about their current travel times. Using a classical commuting scenario, we show that experience sharing can improve convergence times that underlie a typical learning task. Further, we analyse individual learning dynamics to get an impression how aggregate and individual dynamics are related to each other. Based on that interesting pattern of individual learning dynamics can be observed that would otherwise be hidden in an only aggregate analysis.

scholarly journals Agent-Based Optimization for Multiple Signalized Intersections using Q-Learning

2020 ◽  
Author(s):  
Kenneth Tze Kin Teo ◽  
Kiam Beng Yeo ◽  
Yit Kwong Chin ◽  
Helen Sin Ee Chuo ◽  
Min Keng Tan
Keyword(s):  
Signalized Intersections ◽  
Agent Based ◽  
Q Learning
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