Risk Averse Robust Adversarial Reinforcement Learning
Risk Averse Robust Adversarial Reinforcement Learning
Deep reinforcement learning has recently made significant progress in solving computer games and robotic control tasks. A known problem, though, is that policies overfit to the training environment and may not avoid rare, catastrophic events such as automotive accidents. A classical technique for improving the robustness of reinforcement learning algorithms …