Study and Application of Reinforcement Learning in Cooperative Strategy of the Robot Soccer Based on BDI Model

The dynamic cooperation model of multi-Agent is formed by combining reinforcement learning withBDI model. In this model, the concept of the individual optimization loses its meaning, because the repayment ofeach Agent dose not only depend on itsself but also on the choice of other Agents. All Agents can pursue acommon optimum solution and try to realize the united intention as a whole to a maximum limit. The robotmoves to its goal, depending on the present positions of the other robots that cooperate with it and the presentposition of the ball. One of these robots cooperating with it is controlled to move by man with a joystick. In thisway, Agent can be ensured to search for each state-action as frequently as possible when it carries on choosingmovements, so as to shorten the time of searching for the movement space so that the convergence speed ofreinforcement learning can be improved. The validity of the proposed cooperative strategy for the robot soccer hasbeen proved by combining theoretical analysis with simulation robot soccer match (11vs11 ).