RoboCup: A challenge problem for AI and robotics

Abstract. 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 heuristic function that influences the choice of the actions characterizes the HAQL algorithm. A set of empirical evaluations was conducted in the RoboCup 2D Simulator, and experimental results show that even very simple heuristics enhances significantly the performance of the agents.

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“…-Working on obtaining results in more complex domains, such as RoboCup 3D Simulation and Small Size League robots [4].…”

Section: Discussionmentioning

confidence: 99%

Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents

Celiberto

Ribeiro

Costa

et al. 2008

RoboCup 2007: Robot Soccer World Cup XI

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“…The Robocup soccer [6] is a robot soccer tournament with several categories (small size league, middle size league, standard platform league, simulation league, etc.). The Robocup Rescue is based on the Urban Search And Rescue (USAR) benchmark developed by the NIST [7] where robots have to search and rescue the victims of a disaster in a urban environment.…”

Section: Existing Robot Competitions and Benchmarksmentioning

confidence: 99%

Cognitive Systems Platforms using Open Source

Courtney

Michel

Cangelosi

et al. 2009

Performance Evaluation and Benchmarking of Intelligent Systems

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“…We have selected a simplified soccer game consisting of two or three robots as a testbed for the problem because both competitive and cooperative tasks are involved as stated in RoboCup Initiative [4]. We built an original soccer simulator which models real mobile robots we have been using so far in [1,8,9].…”

Section: Mutual Skill Developmentmentioning

confidence: 99%

Cooperative Behavior Acquisition in a Multiple Mobile Robot Environment by Co-evolution

Uchibe

Nakamura

Asada

1999

Lecture Notes in Computer Science

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Abstract. Co-evolution has been receiving increased attention as a method for multi agent simultaneous learning. This paper discusses how multiple robots can emerge cooperative behaviors through co-evolutionary processes. As an example task, a simplified soccer game with three learning robots is selected and a GP (genetic programming) method is applied to individual population corresponding to each robot so as to obtain cooperative and competitive behaviors through evolutionary processes. The complexity of the problem can be explained twofold: co-evolution for cooperative behaviors needs exact synchronization of mutual evolutions, and three robot co-evolution requires well-complicated environment setups that may gradually change from simpler to more complicated situations so that they can obtain cooperative and competitive behaviors simultaneously in a wide range of search area in various kinds of aspects. Simulation results are shown, and a discussion is given.

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RoboCup: A challenge problem for AI and robotics

Cited by 250 publications

References 15 publications

Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents

Heuristic Reinforcement Learning Applied to RoboCup Simulation Agents

Cognitive Systems Platforms using Open Source

Cooperative Behavior Acquisition in a Multiple Mobile Robot Environment by Co-evolution

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