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AUnifiedGame-TheoreticInterpretationof AdversarialRobustness
This paper provides a unified view to explain different adversarial attacks and defensemethods,i.e.
Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning
Temporally-extended actions have been widely used in both learning and planning to improve scalability and reduce complexity.
Appendix 571 In this appendix, we provide more details about the four experiments and some scenario examples
Autoregressive sampling is used to create a traffic snapshot. We train a scenario generation model TrafficGen with mixed data. The detailed hyperparameters are shown in Table 4. Figure 7: Dynamics of the generated traffic scenarios. The first column is the original case. The middle columns show the generated scenarios at different timesteps.
TowardtheFundamentalLimitsofImitation Learning
We then propose a novel algorithm based on minimum-distance functionals in the setting where the transition model is given and the expert is deterministic.Thealgorithmissuboptimalby .|S|H3/2/N,matchingourlower
