2LRL: a two-level multi-agent reinforcement learning algorithm with communication

Erus, Guray
Learning is a key element of an "intelligent" computational system. In Multi- agent Systems (MASs), learning involves acquisition of a cooperative behavior in order to satisfy the joint goals. Reinforcement Learning (RL) is a promising unsupervised machine learning technique inspired from the earlier studies in animal learning. In this thesis, we propose the Two Level Reinforcement Learning with Communication (2LRL) method, a new RL technique to provide cooperative action selection in a multi-agent environment. In 2LRL, the decision mechanism of the agents is divided into two hierarchical levels, in which the agents learn to select their target in the first level and to select the action directed to their target in the second level. The agents communicate their perception to their neighbors and use the communication information in their decision-making. We applied 2LRL method in a hunter-prey environment and observed a satisfactory cooperative behavior.


Intelligent learning system for online learning
Serçe, Fatma Cemile; Alpaslan, Ferda Nur; Jain, Lakhmi (2008-10-01)
The paper presents an Adaptive Intelligent Learning System (AILS) designed to be used with any Learning Management System (LMS). The adaptiveness provides uniquely identifying and monitoring the learner's learning process according to the learner's profile. AILS has been implemented as a multi-agent system. The agents were developed as JADE agents. The paper presents the learning model, system components, agent behavior in learner scenarios, the ontologies used in agent communications, and the adaptive stra...
Reinforcement Learning in Card Game Environments Using Monte Carlo Methods and Artificial Neural Networks
Baykal, Ömer; Alpaslan, Ferda Nur (2019-09-01)
Artificial intelligence has wide range of application areas and games are one of the important ones. There are many applications of artificial intelligence methods in game environments. It is very common for game environments to include intelligent agents. Having intelligent agents makes a game more entertaining and challenging for its players. Reinforcement learning methods can be applied to develop artificial intelligence agents that learn to play a game by themselves without any supervision and can play ...
Pattern recognition in bistable networks
VLADIMIR, CHINAROV; Halıcı, Uğur; Leblebicioğlu, Mehmet Kemal (1999-04-08)
Present study concerns the problem of learning, pattern recognition and computational abilities of a homogeneous network composed from coupled bistable units. An efficient learning algorithm is developed. New possibilities for pattern recognition may be realized due to the developed technique that permits a reconstruction of a dynamical system using the distributions of its attractors. In both cases the updating procedure for the coupling matrix uses the minimization of least-mean-square errors between the ...
Pre-service English as a foreign language teachers' perceptions of the relationship between multiple intelligences and foreign language learning
Savaş, Perihan (2012-12-01)
The relationship between intelligence, language, and learning is a challenging field of study. One way to study how this relationship occurs and works is to investigate the perceptions of advanced language learners. Therefore, this paper reports a study that was conducted to explore 160 pre-service English language teachers' perceptions about which type(s) of multiple intelligence(s) play a role in foreign language learning. The findings of the study indicated that virtually all participants (97%) agreed on...
Machine Learning Models to Enhance the Science of Cognitive Autonomy
MANİ, Ganapathy; BHARGAVA, Bharat; Angın, Pelin; VİLLARREAL VASQUEZ, Miguel; ULYBYSHEV, Denis; KOBES, Jason (2018-09-28)
Intelligent Autonomous Systems (IAS) are highly cognitive, reflective, multitask-able, and effective in knowledge discovery. Examples of IAS include software systems that are capable of automatic reconfiguration, autonomous vehicles, network of sensors with reconfigurable sensory platforms, and an unmanned aerial vehicle (UAV) respecting privacy by deciding to turn off its camera when pointing inside a private residence. Research is needed to build systems that can monitor their environment and interactions...
Citation Formats
G. Erus, “2LRL: a two-level multi-agent reinforcement learning algorithm with communication,” M.S. - Master of Science, Middle East Technical University, 2002.