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Articles 1 - 7 of 7
Full-Text Articles in Physical Sciences and Mathematics
Learning To Play An Imperfect Information Card Game Using Reinforcement Learning, Buğra Kaan Demi̇rdöver, Ömer Baykal, Ferdanur Alpaslan
Learning To Play An Imperfect Information Card Game Using Reinforcement Learning, Buğra Kaan Demi̇rdöver, Ömer Baykal, Ferdanur Alpaslan
Turkish Journal of Electrical Engineering and Computer Sciences
Artificial intelligence and machine learning are widely popular in many areas. One of the most popular ones is gaming. Games are perfect testbeds for machine learning and artificial intelligence with various scenarios and types. This study aims to develop a self-learning intelligent agent to play the Hearts game. Hearts is one of the most popular trick-taking card games around the world. It is an imperfect information card game. In addition to having a huge state space, Hearts offers many extra challenges due to its nature. In order to ease the development process, the agent developed in the scope of this …
Relational-Grid-World: A Novel Relational Reasoning Environment And An Agentmodel For Relational Information Extraction, Faruk Küçüksubaşi, Eli̇f Sürer
Relational-Grid-World: A Novel Relational Reasoning Environment And An Agentmodel For Relational Information Extraction, Faruk Küçüksubaşi, Eli̇f Sürer
Turkish Journal of Electrical Engineering and Computer Sciences
Reinforcement learning (RL) agents are often designed specifically for a particular problem and they generallyhave uninterpretable working processes. Statistical methods-based agent algorithms can be improved in terms ofgeneralizability and interpretability using symbolic artificial intelligence (AI) tools such as logic programming. Inthis study, we present a model-free RL architecture that is supported with explicit relational representations of theenvironmental objects. For the first time, we use the PrediNet network architecture in a dynamic decision-making problemrather than image-based tasks, and multi-head dot-product attention network (MHDPA) as a baseline for performancecomparisons. We tested two networks in two environments -i.e., the baseline box-world environment and …
Multiagent Q-Learning Based Uav Trajectory Planning For Effective Situationalawareness, Erdal Akin, Kubi̇lay Demi̇r, Hali̇l Yetgi̇n
Multiagent Q-Learning Based Uav Trajectory Planning For Effective Situationalawareness, Erdal Akin, Kubi̇lay Demi̇r, Hali̇l Yetgi̇n
Turkish Journal of Electrical Engineering and Computer Sciences
In the event of a natural disaster, arrival time of the search and rescue (SAR) teams to the affected areas is of vital importance to save the life of the victims. In particular, when an earthquake occurs in a geographically large area, reconnaissance of the debris within a short-time is critical for conducting successful SAR missions. An effective and quick situational awareness in postdisaster scenarios can be provided via the help of unmanned aerial vehicles (UAVs). However, off-the-shelf UAVs suffer from the limited communication range as well as the limited airborne duration due to battery constraints. If telecommunication infrastructure is …
Deep Q-Network-Based Noise Suppression For Robust Speech Recognition, Tae-Jun Park, Joon-Hyuk Chang
Deep Q-Network-Based Noise Suppression For Robust Speech Recognition, Tae-Jun Park, Joon-Hyuk Chang
Turkish Journal of Electrical Engineering and Computer Sciences
This study develops the deep Q-network (DQN)-based noise suppression for robust speech recognition purposes under ambient noise. We thus design a reinforcement algorithm that combines DQN training with a deep neural networks (DNN) to let reinforcement learning (RL) work for complex and high dimensional environments like speech recognition. For this, we elaborate on the DQN training to choose the best action that is the quantized noise suppression gain by the observation of noisy speech signal with the rewards of DQN including both the word error rate (WER) and objective speech quality measure. Experiments demonstrate that the proposed algorithm improves speech …
Reinforcement Learning-Based Mobile Robot Navigation, Ni̇hal Altuntaş, Erkan İmal, Nahi̇t Emanet, Ceyda Nur Öztürk
Reinforcement Learning-Based Mobile Robot Navigation, Ni̇hal Altuntaş, Erkan İmal, Nahi̇t Emanet, Ceyda Nur Öztürk
Turkish Journal of Electrical Engineering and Computer Sciences
In recent decades, reinforcement learning (RL) has been widely used in different research fields ranging from psychology to computer science. The unfeasibility of sampling all possibilities for continuous-state problems and the absence of an explicit teacher make RL algorithms preferable for supervised learning in the machine learning area, as the optimal control problem has become a popular subject of research. In this study, a system is proposed to solve mobile robot navigation by opting for the most popular two RL algorithms, Sarsa($\lambda )$ and Q($\lambda )$. The proposed system, developed in MATLAB, uses state and action sets, defined in a …
Multiagent-Based Simulation Of Simultaneous Electricity Market Auctions In Restructured Environment, Mohammad Farshad
Multiagent-Based Simulation Of Simultaneous Electricity Market Auctions In Restructured Environment, Mohammad Farshad
Turkish Journal of Electrical Engineering and Computer Sciences
In the restructured environment of the power industry, various commodities such as energy and operating reserves may be provided through simultaneous auctions. Prediction of market players' behavior in the auctions and simulation of the markets' environment can assist market decision-makers in evaluating specific policies before enforcing them in the real environment. Considering effects of the energy and varieties of reserve markets and also their interactions in the simulations is of high importance, which leads to more realistic simulation results. In this paper, an approach based on a multiagent system is proposed for simulating the simultaneous energy, spinning reserve, and replacement …
Actor-Critic-Based Ink Drop Spread As An Intelligent Controller, Hesam Sagha, Iman Esmaili Paeen Afrakoti, Saeed Bagherishouraki
Actor-Critic-Based Ink Drop Spread As An Intelligent Controller, Hesam Sagha, Iman Esmaili Paeen Afrakoti, Saeed Bagherishouraki
Turkish Journal of Electrical Engineering and Computer Sciences
This paper introduces an innovative adaptive controller based on the actor-critic method. The proposed approach employs the ink drop spread (IDS) method as its main engine. The IDS method is a new trend in soft-computing approaches that is a universal fuzzy modeling technique and has been also used as a supervised controller. Its process is very similar to the processing system of the human brain. The proposed actor-critic method uses an IDS structure as an actor and a 2-dimensional plane, representing control variable states, as a critic that estimates the lifetime goodness of each state. This method is fast, simple, …