WrightEagle 2D Soccer Simulation Team is a branch of WrightEagle Robocup Team, established in 1998 by the Multi-Agent Systems Lab., University of Science and Technology of China (USTC). We have participated in annual competitions of RoboCup since 1999 and have won 6 world champions and 5 runners-up of RoboCup since 2005.
We model RoboCup 2D soccer simulation as a typical problem of partially observable stochastic games (POSGs). In order to solve it online under realtime constraints, we take advantage of rationality assumption, hierarchical decomposition, state abstraction, expectimax tree search, Monte Carlo simulation and heuristic evaluation. More precisely, we model the RoboCup 2D game as a Markov decision process (MDP), assuming that opponents and teammates are all rational. We further decompose the sequential decision-making problem within the resulting MDP into a hierarchy of subtasks, including attack, defense, shoot, dribble, pass, intercept, position, mark, block, formation, etc. A hierarchical online planning algorithm is then developed for the agent to do online planning in realtime, exploiting subtask-specific heuristic and evaluation functions. More information regarding our approach to RoboCup 2D can be found in our recent publications.
** Updates **
- This is the first release of the base code of WrightEagle 2D team. -- 08/13/2011
- Please refer to https:/
In the 2D Simulation League, two teams of eleven autonomous software programs (called agents) each play soccer in a two-dimensional virtual soccer stadium represented by a central server, called SoccerServer. This server knows everything about the game, i.e. the current position of all players and the ball, the physics and so on. The game further relies on the communication between the server and each agent. On the one hand each player receives relative and noisy input of his virtual sensors (visual, acustic and physical) and may on the other hand perform some basic commands (like dashing, turning or kicking) in order to influence its environment. The big challenge in the Simulation League is to conclude from all possible world states (derived from the sensor input by calculating a sight on the world as absolute and noise-free as possible) to the best possible action to execute. As a game is divided into 6000 cycles this task has to be accomplished in time slot of 100 ms (the length of each cycle). Further information and the SoccerServer software can be found at http://
 Online planning for large Markov decision processes with hierarchical decomposition, Aijun Bai, Feng Wu, and Xiaoping Chen, ACM Transactions on Intelligent Systems and Technology (ACM TIST),6(
 Towards a Principled Solution to Simulated Robot Soccer, Aijun Bai, Feng Wu, and Xiaoping Chen, RoboCup-2012: Robot Soccer World Cup XVI, Lecture Notes in Artificial Intelligence, Vol. 7500, Springer Verlag, Berlin, 2013.
 Online Planning for Large MDPs with MAXQ Decomposition, Aijun Bai, Feng Wu, and Xiaoping Chen, AAMAS 2012 Autonomous Robots and Multirobot Systems Workshop (ARMS), Valencia, Spain, June 2012.
 Online Planning for Large MDPs with MAXQ Decomposition (Extended Abstract), Aijun Bai, Feng Wu, and Xiaoping Chen, Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), Valencia, Spain, June 2012.
 WrightEagle and UT Austin Villa: RoboCup 2011 Simulation League Champions, Aijun Bai, Xiaoping Chen, Patrick MacAlpine, Daniel Urieli, Samuel Barrett, and Peter Stone, RoboCup-2011: Robot Soccer World Cup XV, Lecture Notes in Artificial Intelligence, Vol. 7416, Springer Verlag, Berlin, 2012.