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- Reinaldo A. C. Bianchi, Carlos H. C. Ribeiro, Anna Helena Reali Costa
- J. Heuristics
- 2008

This paper investigates how to make improved action selection for online policy learning in robotic scenarios using reinforcement learning (RL) algorithms. Since finding control policies using any RL algorithm can be very time consuming, we propose to combine RL algorithms with heuristic functions for selecting promising actions during the learning process.… (More)

This work presents a new algorithm, called Heuris-tically Accelerated Minimax-Q (HAMMQ), that allows the use of heuristics to speed up the well-known Multiagent Reinforcement Learning algorithm Minimax-Q. A heuristic function H that influences the choice of the actions characterises the HAMMQ algorithm. This function is associated with a preference policy… (More)

This work presents a new algorithm, called Heuristically Accelerated Q–Learning (HAQL), that allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q–learning. A heuristic function H that influences the choice of the actions characterizes the HAQL algorithm. The heuristic function is strongly associated with the policy: it… (More)

This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and Reinforcement Learning (RL) techniques. This approach, called Case Based Heuristically Accelerated Reinforcement Learning (CB-HARL), builds upon an emerging technique, the… (More)

- Reinaldo A. C. Bianchi, Anna H. R. C. Rillo

This paper describes a purposive computer vision system for visually guided tasks and a Multi-Agent architecture used to model it. In this architecture, the vision system's purpose is decomposed into a set of behaviors, which are translated into specific tasks. Purpose, behaviors and tasks, as well as the relationship among them, are modeled using a… (More)

Since finding control policies using Reinforcement Learning (RL) can be very time consuming, in recent years several authors have investigated how to speed up RL algorithms by making improved action selections based on heuristics. In this work we present new theoretical results – convergence and a superior limit for value estimation errors – for the class… (More)

- Reinaldo A. C. Bianchi, Murilo Fernandes Martins, Carlos H. C. Ribeiro, Anna Helena Reali Costa
- IEEE Trans. Cybernetics
- 2014

This paper presents a novel class of algorithms, called Heuristically-Accelerated Multiagent Reinforcement Learning (HAMRL), which allows the use of heuristics to speed up well-known multiagent reinforcement learning (RL) algorithms such as the Minimax-Q. Such HAMRL algorithms are characterized by a heuristic function, which suggests the selection of… (More)

This paper describes the design and implementation of robotic agents for the RoboCup Simulation 2D category that learns using a recently proposed Heuristic Reinforcement Learning algorithm, the Heuris-tically Accelerated Q–Learning (HAQL). This algorithm allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q–Learning. A… (More)

In this paper we propose to combine three AI techniques to speed up a Reinforcement Learning algorithm in a Transfer Learning problem: Case-based Reasoning, Heuristically Accelerated Reinforcement Learning and Neural Networks. To do so, we propose a new algorithm, called L3, which works in 3 stages: in the first stage, it uses Reinforcement Learning to… (More)

This paper presents a Multi-Robot Task Allocation (MRTA) system, implemented on a RoboCup Small Size League team, where robots participate of auctions for the available roles, such as attacker or defender, and use Heuristically Accelerated Reinforcement Learning to evaluate their aptitude to perform these roles, given the situation of the team, in… (More)