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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 Heuristically AcceleratedMinimax-Q (HAMMQ), that allows the use of heuristics to speed up the wellknown 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 that… (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 functionH that influences the choice of the actions characterizes the HAQL algorithm. The heuristic function is strongly associated with the policy: it… (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)

- Carlos H. C. Ribeiro
- Journal of Intelligent and Robotic Systems
- 1998

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 Heuristically Accelerated Q–Learning (HAQL). This algorithm allows the use of heuristics to speed up the well-known Reinforcement Learning algorithm Q–Learning. A… (More)

Reinforcement Learning (RL) is learning through direct experimentation. It does not assume the existence of a teacher that provides examples upon which learning of a task takes place. Instead, in RL experience is the only teacher. With historical roots on the study of conditioned reflexes, RL soon attracted the interest of Engineers and Computer Scientists… (More)

This paper investigates the use of experience generalization on concurrent and on-line policy learning in multi-agent scenarios, using reinforcement learning algorithms. Agents learning concurrently implies in a non-stationary scenario, since the reward received by one agent (for applying an action in a state) depends on the behavior of the other agents.… (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)