Modeling a system for monitoring an object using artificial neural networks and reinforcement learning

Peixoto, Helton Maia; Diniz, A. A. R.; Almeida, Nuno; Melo, Jorge Dantas de; Neto, Adrião Duarte Dória; Guerreiro, Ana M. G.

doi:10.1109/ijcnn.2011.6033519

Cited by 3 publications

(2 citation statements)

References 10 publications

(16 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The goal of the RL method is to guide the agent towards taking actions that would result in maximizing (or minimizing) the sum of the reinforcement signals (numerical reward or punishment) received over the course of time, known as the expected return, which does not always signify maximizing the immediate reinforcement to be received [ 34 ].…”

Section: Reinforcement Learningmentioning

confidence: 99%

“…The behavior that the agent should adopt in order to achieve maximization (or minimization) of the return is known as the policy and can be expressed by π. According to [ 34 ], a policy π (s,a) is a mapping of states ( s ) in actions ( a ) taken in that state, and represents the probability of selecting each one of the possible actions, in such a way that the best actions correspond to the greatest probabilities of selection. When this mapping maximizes the sum of the rewards, the optimum policy has been achieved.…”

Section: Reinforcement Learningmentioning

confidence: 99%

See 1 more Smart Citation

Beamforming and Power Control in Sensor Arrays Using Reinforcement Learning

Almeida

Fernandes

Neto

2015

Sensors

View full text Add to dashboard Cite

The use of beamforming and power control, combined or separately, has advantages and disadvantages, depending on the application. The combined use of beamforming and power control has been shown to be highly effective in applications involving the suppression of interference signals from different sources. However, it is necessary to identify efficient methodologies for the combined operation of these two techniques. The most appropriate technique may be obtained by means of the implementation of an intelligent agent capable of making the best selection between beamforming and power control. The present paper proposes an algorithm using reinforcement learning (RL) to determine the optimal combination of beamforming and power control in sensor arrays. The RL algorithm used was Q-learning, employing an ε-greedy policy, and training was performed using the offline method. The simulations showed that RL was effective for implementation of a switching policy involving the different techniques, taking advantage of the positive characteristics of each technique in terms of signal reception.

show abstract

Section: Reinforcement Learningmentioning

confidence: 99%

Section: Reinforcement Learningmentioning

confidence: 99%