Reinforcement distribution in fuzzy Q-learning

Bonarini, Andrea; Lazaric, Alessandro; Montrone, Francesco; Restelli, Marcello

doi:10.1016/j.fss.2008.11.026

Cited by 46 publications

(25 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1. Q-learning has been used for learning fuzzy systems [21]. Since the state and actions of Q-learning algorithm can be set by fuzzy variables, Q-learning can take advantage of fuzziness.…”

Section: Proposed Methodsmentioning

confidence: 99%

Traffic light control based on fuzzy Q-leaming

Moghaddam

Hosseini

Safabakhsh

2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

View full text Add to dashboard Cite

Traffic is an issue that many big cities are confronted with because of ever-increasing population growth. In this paper we propose a two phase traffic light control system based on fuzzy Q-learning for an isolated 4-way intersection. The states and actions of the Q-learning variables is set by a fuzzy algorithm which can be learned through environmental interactions and taking advantage of fuzzy logic. The proposed algorithm was simulated for a period of one hour for each of 14 different traffic conditions. Comparison with other methods was carried out on the 14 traffic conditions. The results showed that the proposed algorithms decrease the total waiting time and the mean of queue length.Index Terms-Fuzzy logic, fuzzy Q-learning, traffic light control.

show abstract

“…1. Q-learning has been used for learning fuzzy systems [21]. Since the state and actions of Q-learning algorithm can be set by fuzzy variables, Q-learning can take advantage of fuzziness.…”

Section: Proposed Methodsmentioning

confidence: 99%

Traffic light control based on fuzzy Q-leaming

Moghaddam

Hosseini

Safabakhsh

2015

2015 the International Symposium on Artificial Intelligence and Signal Processing (AISP)

View full text Add to dashboard Cite

show abstract

“…The partial overlapping of nearby linguistic variables of a fuzzified input gives fuzzy Q-learning an improved flexibility in addition to robustness and smoothness [19]. In the context of this paper, small cells (RL agents) tend to learn optimal CREO values (actions) for each of the fuzzy rules through iterative interaction with their environment, which includes dynamic radio conditions, temporal and spatial fluctuations in users' traffic, and backhaul capacity variations.…”

Section: Proposed Fuzzy Q-learning-based User-centricmentioning

confidence: 99%

Fuzzy Q-learning-based user-centric backhaul-aware user cell association scheme

Pervez

Jaber

Qadir

et al. 2017

2017 13th International Wireless Communications and Mobile Computing Conference (IWCMC)

View full text Add to dashboard Cite

Abstract-Heterogeneous networks are a key solution to serving the exponential surge in data volume and higher quality expectations. Nonetheless, such networks require the ubiquitous presence of fiberto-the-cell to address the performance demands of 5G and fastspreading small cells. To this end, innovative ways of optimizing the usage of realistic backhaul links are being investigated. In this work, we propose a fuzzy Q-learning-based user-centric backhaul-aware user cell association scheme.The proposed scheme aims at optimizing the user-cell association process in a context-aware and backhaul-aware manner. Complementing the scheme with fuzzy-logic requires 33.3% additional storage memory. On the other hand, it increases the computational efficiency by 60% and improves the users' performance by 12%.

show abstract

“…By using the standard Q-learning method for the decision making of an agent, a table must be set up to store each pair of state and action. The Q-value can be calculated by [20], [29] (1)…”

Section: A the Maximum Mapping Value Function Of The Proposed Approachmentioning

confidence: 99%

“…For example, the reward function based on the weighted sum is complicated, which need to calculate the weights of the factors [9]. Some reward values obtained by the methods based on fuzzy rules are discrete and the number of them is limited, which are not suitable to the complex system, because the reward values are continuous and unlimited in realtime applications [20]. To deal with the problems above, an adaptive reward value function is proposed, which is introduced as follows: (7) where is the reward value for the th agent; is the number of influence factors (the influence factors in this paper are the indexes for water resource optimal allocation, such as water quality and economic benefit index); is the th influence factor; is an action (decision), and is a benefit function used to calculate the benefit of the influence factor under the certain action , which is determined by the actual application.…”

Section: The Adaptive Multifactor Reward Value Functionmentioning

confidence: 99%

“…The Q-learning-based method is one of the most popular reinforcement learning methods, which is widely used to learn tabular relationships among states, described by a finite number of values for each variable, and discrete action [20], [21]. Recently, there has been a lot of research on the Q-learning-based method for various optimization problems.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Multiagent Q-Learning-Based Optimal Allocation Approach for Urban Water Resource Management System

Liu

Ren

et al. 2014

IEEE Trans. Automat. Sci. Eng.

View full text Add to dashboard Cite

Water environment system is a complex system, and an agent-based model presents an effective approach that has been implemented in water resource management research. Urban water resource optimal allocation is a challenging and critical issue in water environment systems, which belongs to the resource optimal allocation problem. In this paper, a novel approach based on multiagent Q-learning is proposed to deal with this problem. In the proposed approach, water users of different regions in the city are abstracted into the agent-based model. To realize the cooperation among these stakeholder agents, a maximum mapping value function-based Q-learning algorithm is proposed in this study, which allows the agents to self-learn. In the proposed algorithm, an adaptive reward value function is used to improve the performance of the multiagent Q-learning algorithm, where the influence of multiple factors on the optimal allocation can be fully considered. The proposed approach can deal with various situations in urban water resource allocation. The experimental results show that the proposed approach is capable of allocating water resource efficiently and the objectives of all the stakeholder agents can be successfully achieved.Note to Practitioners-Water resource optimal allocation is an important decision making activity in water resource management systems. This paper sets up a water resource optimal allocation model based on multiagent modeling technology, where different optimal objectives are abstracted into various properties of agents, and a new multiagent Q-learning approach is proposed to deal with the optimal allocation problem in water resource management system. The proposed approach can be used in practical water resource management systems, with any feasible data from the statistical agencies of government and companies. The experimental results demonstrate the effectiveness and efficiency of the agent-based allocation model and the proposed approach based on the novel multiagent Q-learning algorithm.Index Terms-Complex system, multiagent Q-learning, optimal allocation, urban water resource management.

show abstract

Reinforcement distribution in fuzzy Q-learning

Cited by 46 publications

References 15 publications

Traffic light control based on fuzzy Q-leaming

Traffic light control based on fuzzy Q-leaming

Fuzzy Q-learning-based user-centric backhaul-aware user cell association scheme

A Multiagent Q-Learning-Based Optimal Allocation Approach for Urban Water Resource Management System

Contact Info

Product

Resources

About