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In this paper, we address an improved Bayesian inverse reinforcement learning (BIRL) algorithm for behavior decision making problem of unmanned ground vehicle through learning from teaching sequences. It is demonstrated that in this paper how the autonomous drving vehicle executes optimal action under current state according to expert’s demonstration by using the proposed approach. Simulation tests show that the proposed improved BIRL can make reasonable decisions for the driverless car.

Unmanned Ground Vehicle Behavior Decision via Improved Bayesian Inverse Reinforcement Learning