The course will provide an overview of recent research in Adversarial Reinforcement Learning (RL). Research papers covered in the course will showcase the landscape of attacks on RL agents and the optimal attack strategies, which is crucial for understanding security threats against the deployed systems. In particular, the research papers will cover optimal attack strategies for test-time, training-time, and backdoor attacks on RL agents. After this course, the participants will gain a better perspective of important problems for developing robust and secure algorithms in sequential decision-making settings. More details will be provided at the end of October 2021.