Abstract: An intelligent obstacle avoidance scheme for unmanned vehicle based on reinforcement learning is proposed in this paper. In view of that the movement of unmanned vehicle must meet both interior and exterior constraints, including vehicle dynamics constraints and traffic rule constraints and its output must be continuous, which the traditional reinforcement learning cannot assure, an improved deep deterministic policy gradient algorithm is proposed to tackle continuous motion space issue and achieve the continuous output of steering wheel angle and acceleration. Multi-source sensor data fusion is adopted to fulfill the state input of unmanned vehicle obstacle avoidance algorithm and both interior and exterior constraints are added to make output motion more reasonable and effective. Finally a simulation is conducted on the open-source simulation platform TORCS and the effectiveness and robustness of the algorithm verified

Key words: unmanned vehicle, obstacle avoidance, reinforcement learning, TORCS