Relating Ant Colony Optimisation and Reinforcement Learning Interim Report

نویسنده

  • Tim Kovacs
چکیده

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Ants, stochastic optimisation and reinforcement learning

Ant colonies are successful and resilient biological entities, which exhibit a number of desirable collective problem-solving behaviours. The study of ant colonies has recently inspired the development of artificial algorithms for stochastic optimisation and adaptive control, which attempt to mimic some of the properties of the biological counterpart. In this paper, we give a brief overview of ...

متن کامل

Optimisation sous contraintes par intelligence collective auto-adaptative. (Strong combination of ant colony optimization with constraint programming optimization)

We introduce an approach which combines ACO (Ant Colony Optimization) and IBM ILOG CP Optimizer for solving COPs (Combinatorial Optimization Problems). The problem is modeled using the CP Optimizer modeling API. Then, it is solved in a generic way by a two-phase algorithm. The first phase aims at creating a hot start for the second: it samples the solution space and applies reinforcement learni...

متن کامل

Artificial Ant Colonies and E-Learning: An Optimisation of Pedagogical Paths

This paper describes current research on the optimisation of the pedagogical path of a student in an existing e-learning software. This optimisation is performed following the models given by a fairly recent field of Artificial Intelligence: Ant Colony Optimisation (ACO) [1,2,4]. The underlying structure of the E-learning material is represented by a graph with valued arcs whose weights are opt...

متن کامل

Scaling Ant Colony Optimization with Hierarchical Reinforcement Learning Partitioning THESIS

This research merges the hierarchical reinforcement learning (HRL) domain and the ant colony optimization (ACO) domain. The merger produces a HRL ACO algorithm capable of generating solutions for both domains. This research also provides two specific implementations of the new algorithm: the first a modification to Dietterich’s MAXQ-Q HRL algorithm, the second a hierarchical ACO algorithm. Thes...

متن کامل

Convergence analysis of ant colony learning

In this paper, we study the convergence of the pheromone levels of Ant Colony Learning (ACL) in the setting of discrete state spaces and noiseless state transitions. ACL is a multi-agent approach for learning control policies that combines some of the principles found in ant colony optimization and reinforcement learning. Convergence of the pheromone levels in expected value is a necessary requ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2007