This work presents a new algorithm, called Heuris- tically Accelerated Minimax-Q (HAMMQ), that al- lows the use of heuristics to speed up the well- known Multiagent Reinforcement Learning algo- rithm Minimax-Q. A heuristic function H that in- fluences the choice of the actions characterises the HAMMQ algorithm. This function is associated with a preference policy that indicates that a cer- tain action must be taken instead of another. A set of empirical evaluations were conducted for the proposed algorithm in a simplified simulator for the robot soccer domain, and experimental results show that even very simple heuristics enhances sig- nificantly the performance of the multiagent rein- forcement learning algorithm.