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Document type:
Konferenzbeitrag 
Author(s):
Xiao Wang; Saasha Nair; Matthias Althoff 
Title:
Falsification-Based Robust Adversarial Reinforcement Learning 
Abstract:
Reinforcement learning (RL) has achieved enormous progress in solving various sequential decision-making problems, such as control tasks in robotics. Since policies are overfitted to training environments, RL methods have often failed to be generalized to safety-critical test scenarios. Robust adversarial RL (RARL) was previously proposed to train an adversarial network that applies disturbances to a system, which improves the robustness in test scenarios. However, an issue of neural network-bas...    »
 
Book / Congress title:
Proc. of IEEE International Conference on Machine Learning and Applications 
Year:
2020