Improving deep reinforcement learning by safety guarding model via hazardous experience planning

Graphical abstract

References

[1] Kai A , Deisenroth M P , Brundage M , Bharath A A . Deep reinforcement learning: a brief survey. IEEE Signal Processing Magazine, 2017, 34( 6): 26– 38

[2] Cheng R, Orosz G, Murray R M, Burdick J W. End-to-end safe reinforcement learning through barrier functions for safety-critical continuous control tasks. In: Proceedings of the AAAI Conference on Artificial Intelligence. 2019, 3387−3395

[3] Saunders W, Sastry G, Stuhlmueller A, Evans O. Trial without error: towards safe reinforcement learning via human intervention. In: Proceedings of the 17th International Conference on Autonomous Agents and MultiAgent Systems. 2018, 2067−2069

[4] Achiam J, Held D, Tamar A, Abbeel P. Constrained policy optimization. In: Proceedings of the International Conference on Machine Learning. 2017, 22– 31

[5] García J , Fernández F . A comprehensive survey on safe reinforcement learning. Journal of Machine Learning Research, 2015, 16 : 1437– 1480

[6] Chatzilygeroudis K , Vassiliades V , Mouret J B . Reset-free trial-and-error learning for robot damage recovery. Robotics and Autonomous Systems, 2018, 100 : 236– 250

[7] Zhu F , Wu W , Fu Y , Liu Q . A dual deep network based secure deep reinforcement learning method. Chinese Journal of Computers, 2019, 42( 8): 1812– 1826

RIGHTS & PERMISSIONS

Higher Education Press