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hierarchical-reinforcement-learning
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Automatic abstraction in reinforcement learning using ant system algorithm
, Article AAAI Spring Symposium - Technical Report ; Volume SS-13-05 , 2013 , Pages 9-14 ; 9781577356028 (ISBN) ; Taghizadeh, N ; Beigy, H ; Sharif University of Technology
2013
Abstract
Nowadays developing autonomous systems, which can act in various environments and interactively perform their assigned tasks, are intensively desirable. These systems would be ready to be applied in different fields such as medicine, controller robots and social life. Reinforcement learning is an attractive area of machine learning which addresses these concerns. In large scales, learning performance of an agent can be improved by using hierarchical Reinforcement Learning techniques and temporary extended actions. The higher level of abstraction helps the learning agent approach lifelong learning goals. In this paper a new method is presented for discovering subgoal states and constructing...
A new method for discovering subgoals and constructing options in reinforcement learning
, Article Proceedings of the 5th Indian International Conference on Artificial Intelligence, IICAI 2011 ; 2011 , Pages 441-450 ; 9780972741286 (ISBN) ; Beigy, H ; SIT; Saint Mary's University; EKLaT Research; Infobright ; Sharif University of Technology
Abstract
In this paper the problem of automatically discovering subtasks and hierarchies in reinforcement learning is considered. We present a novel method that allows an agent to autonomously discover subgoals and create a hierarchy from actions. Our method identifies subgoals by partitioning local state transition graphs. Options constructed for reaching these subgoals are added to action choices and used for accelerating the Q-Learning algorithm. Experimental results show significant performance improvements, especially in the initial learning phase
Optimal Process Planning for Automated Robotic Assembly of Mechanical Assembles based on Reinforcement Learning Method
, M.Sc. Thesis Sharif University of Technology ; Khodaygan, Saeed (Supervisor)
Abstract
Nowadays, the assembly process is planned by an expert and requires knowledge and it is time-consuming. The flexibility and optimality of the assembly plan depend on the knowledge and creativity of the expert, and therefore expertise is an important parameter in developing the assembly plan. Therefore, the use of intelligent methods to plan the assembly process has been considered by many researchers. . The reinforcing learning approach has the potential to solve complex problems due to the use of experience gained from interacting with the environment and Has been successfully implemented in controlling many robotic tasks. However, due to the inherent complexity of the assembly, as well as...
Brain Inspired Meta Reinforcement Learning Using Brain-Inspired Networks
, M.Sc. Thesis Sharif University of Technology ; Soleymani Baghshahi, Mahdih (Supervisor)
Abstract
Reinforcement learning is one of the most well-known learning paradigms in biological agents and one of the most used ones for solving plenty of problems. One of the reasons for this widespread use is the low demand for supervising signals. However, the sparsity of the reward signal causes increasing in sample complexity that needs for learning new tasks. This issue makes trouble in multi-task settings, specifically.One of the most promising approaches to learning new tasks by limited interaction with the environment is meta reinforcement learning. An approach in which fast adaption becomes possible by limiting hypothesis space and creating inductive biases by learning meta parameters....
A graph-theoretic approach toward autonomous skill acquisition in reinforcement learning
, Article Evolving Systems ; Volume 9, Issue 3 , 2018 , Pages 227-244 ; 18686478 (ISSN) ; Taghizadeh, N ; Beigy, H ; Sharif University of Technology
Springer Verlag
2018
Abstract
Hierarchical reinforcement learning facilitates learning in large and complex domains by exploiting subtasks and creating hierarchical structures using these subtasks. Subtasks are usually defined through finding subgoals of the problem. Providing mechanisms for autonomous subgoal discovery and skill acquisition is a challenging issue in reinforcement learning. Among the proposed algorithms, a few of them are successful both in performance and also efficiency in terms of the running time of the algorithm. In this paper, we study four methods for subgoal discovery which are based on graph partitioning. The idea behind the methods proposed in this paper is that if we partition the transition...
Using strongly connected components as a basis for autonomous skill acquisition in reinforcement learning
, Article 6th International Symposium on Neural Networks, ISNN 2009, Wuhan, 26 May 2009 through 29 May 2009 ; Volume 5551 LNCS, Issue PART 1 , 2009 , Pages 794-803 ; 03029743 (ISSN); 3642015069 (ISBN); 9783642015069 (ISBN) ; Beigy, H ; Sharif University of Technology
2009
Abstract
Hierarchical reinforcement learning (HRL) has had a vast range of applications in recent years. Preparing mechanisms for autonomous acquisition of skills has been a main topic of research in this area. While different methods have been proposed to achieve this goal, few methods have been shown to be successful both in performance and also efficiency in terms of time complexity of the algorithm. In this paper, a linear time algorithm is proposed to find subgoal states of the environment in early episodes of learning. Having subgoals available in early phases of a learning task, results in building skills that dramatically increase the convergence rate of the learning process. © 2009 Springer...