Cooperative Q-learning techniques for distributed online power allocation in femtocell networks

Artificial Intelligence allows to create engines that are able to explore, learn environments and therefore create policies that permit to control them in real time with no human intervention. It can be applied, through its Reinforcement Learning techniques component, using frameworks such as temporal differences, State-Action-Reward-State-Action (SARSA), Q Learning to name a few, to systems that are be perceived as a Markov Decision Process, this opens door in front of applying Reinforcement Learning to Cloud Load Balancing to be able to dispatch load dynamically to a given Cloud System. The authors will describe different techniques that can used to implement a Reinforcement Learning based engine in a cloud system.

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An Adaptive Spectrum Allocation Algorithm in Femtocell Networks Using Q-learning

Information Technology Journal ◽

10.3923/itj.2013.7353.7360 ◽

2013 ◽

Vol 12 (23) ◽

pp. 7353-7360

Author(s):

Bi Yu-ting ◽

Chen Xin ◽

Wang Hong-lu ◽

Liu Zong-Qi

Keyword(s):

Spectrum Allocation ◽

Femtocell Networks ◽

Allocation Algorithm ◽

Q Learning

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Water-Filling Power Allocation Algorithm for Joint Utility Optimization in Femtocell Networks

GLOBECOM 2017 - 2017 IEEE Global Communications Conference ◽

10.1109/glocom.2017.8254060 ◽

2017 ◽

Cited By ~ 1

Author(s):

Bo Gu ◽

Mianxiong Dong ◽

Zhi Liu ◽

Cheng Zhang ◽

Yoshiaki Tanaka

Keyword(s):

Power Allocation ◽

Femtocell Networks ◽

Allocation Algorithm ◽

Utility Optimization ◽

Water Filling

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Towards Better Interpretability in Deep Q-Networks

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33014561 ◽

2019 ◽

Vol 33 ◽

pp. 4561-4569 ◽

Cited By ~ 2

Author(s):

Raghuram Mandyam Annasamy ◽

Katia Sycara

Keyword(s):

Neural Network ◽

Network Architecture ◽

Empirical Studies ◽

Superior Performance ◽

Training Algorithms ◽

Neural Network Architecture ◽

Q Learning ◽

The Neural Network ◽

Learning Techniques ◽

Out Of Sample

Deep reinforcement learning techniques have demonstrated superior performance in a wide variety of environments. As improvements in training algorithms continue at a brisk pace, theoretical or empirical studies on understanding what these networks seem to learn, are far behind. In this paper we propose an interpretable neural network architecture for Q-learning which provides a global explanation of the model’s behavior using key-value memories, attention and reconstructible embeddings. With a directed exploration strategy, our model can reach training rewards comparable to the state-of-the-art deep Q-learning models. However, results suggest that the features extracted by the neural network are extremely shallow and subsequent testing using out-of-sample examples shows that the agent can easily overfit to trajectories seen during training.

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Implementation of modified Q learning technique in EMCAP control architecture

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i1.5.9160 ◽

2017 ◽

Vol 7 (1.5) ◽

pp. 269

Author(s):

D. Ganesha ◽

Vijayakumar Maragal Venkatamuni

Keyword(s):

Learning Algorithm ◽

Control Strategies ◽

Cognitive Architecture ◽

Dynamic Environment ◽

Learning System ◽

Selection Strategy ◽

Q Learning ◽

Learning Techniques ◽

Markov Decision ◽

Environment Experiment

This research introduces a self learning modified (Q-Learning) techniques in a EMCAP (Enhanced Mind Cognitive Architecture of pupils). Q-learning is a modelless reinforcement learning (RL) methodology technique. In Specific, Q-learning can be applied to establish an optimal action-selection strategy for any respective Markov decision process. In this research introduces the modified Q-learning in a EMCAP (Enhanced Mind Cognitive Architecture of pupils). EMCAP architecture [1] enables and presents various agent control strategies for static and dynamic environment. Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance.his modified q learning algorithm can be more suitable in EMCAP architecture. The experiments are conducted the modified Q-Learning system gets more rewards compare to existing Q-learning.

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