Reinaldo A. C. Bianchi

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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 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)
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 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)
A time critical process in a real-time mobile robot application such as RoboCup, is the determination of the robot position in the game field. Aiming at low-cost and efficiency, this paper proposes the use of field-programmable gate array device (FPGA) in the vision system of a robotic team. We describe the translation of well-known computer vision(More)
Selecting which algorithms should be used by a mobile robot computer vision system is a decision that is usually made a priori by the system developer, based on past experience and intuition , not systematically taking into account information that can be found in the images and in the visual process itself to learn which algorithm should be used, in(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)