Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning With Application to Autonomous Driving
Published in IEEE Robotics and Automation Letters, 2020
Recommended citation: Zheng. Wu, Liting. Sun, Wei. Zhan, Chenyu. Yang and Masayoshi Tomizuka, "Efficient Sampling-Based Maximum Entropy Inverse Reinforcement Learning With Application to Autonomous Driving," in IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 5355-5362, Oct. 2020
Present an efficient sampling-based maximum-entropy inverse reinforcement learning (IRL) algorithm to extract what human drivers try to optimize from real traffic data.