UvA Trilearn 2003 Team Description
نویسندگان
چکیده
This paper describes the main features of the UvA Trilearn soccer simulation team, which participated for the first time at the RoboCup-2001 competition. The main concepts of the previous teams will be addressed, followed by the improvements introduced in UvA Trilearn 2003. These include an extension of the intercept skill, improved passing behavior and especially the usage of coordination graphs to specify the coordination requirements between the different agents. Finally, we will give some conclusions and describe future research directions.
منابع مشابه
UvA Trilearn 2002 Team Description
This paper describes the main features of the UvA Trilearn 2002 soccer simulation team. This team is an extension over UvA Trilearn 2001 which participated for the first time at the RoboCup-2001 competition. The main concepts of UvA Trilearn 2001 will be addressed briefly, followed by the improvements introduced in UvA Trilearn 2002. These include the improved localization methods using particl...
متن کاملThe Incremental Development of a Synthetic Multi-Agent System: The UvA Trilearn 2001 Robotic Soccer Simulation Team
This thesis describes the incremental development and main features of a synthetic multi-agent system called UvA Trilearn 2001. UvA Trilearn 2001 is a robotic soccer simulation team that consists of eleven autonomous software agents. It operates in a physical soccer simulation system called soccer server which enables teams of autonomous software agents to play a game of soccer against each oth...
متن کاملLearning of Soccer Player Agents Using a Policy Gradient Method: Pass Selection
This research develops a learning method for the pass selection problem of midfielders in RoboCup Soccer Simulation games. A policy gradient method is applied as a learning method to solve this problem because it can easily represent the various heuristics of pass selection in a policy function. We implement the learning function in the midfielders’ programs of a well-known team, UvA Trilearn B...
متن کاملHeuristic Q-Learning Soccer Players: A New Reinforcement Learning Approach to RoboCup Simulation
This paper describes the design and implementation of a 4 player RoboCup Simulation 2D team, which was build by adding Heuristic Accelerated Reinforcement Learning capabilities to basic players of the well-known UvA Trilearn team. The implemented agents learn by using a recently proposed Heuristic Reinforcement Learning algorithm, the Heuristically Accelerated Q–Learning (HAQL), which allows th...
متن کاملA Scoring Policy for Simulated Soccer Agents Using Reinforcement Learning
The robotic soccer is one of the complex multi-agent systems in which agents play the role of soccer players. The characteristics of such systems are: real-time, noisy, collaborative and adversarial. Because of the inherent complexity of this type of systems, machine learning is used for training agents. Since the main purpose of a soccer game is to score goals, it is important for a robotic so...
متن کامل