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Approximate Information State for Approximate Planning and Reinforcement Learning in Partially Observed Systems., , , and . J. Mach. Learn. Res., (2022)Mean-field games among teams., , and . CoRR, (2023)Counterfactual Explanation Policies in RL., , , , and . CoRR, (2023)Differentiable Agent-based Epidemiology., , , , , and . CoRR, (2022)DeepABM: Scalable and Efficient Agent-Based Simulations Via Geometric Learning Frameworks - a Case Study For Covid-19 Spread and Interventions., , , , , , , and . WSC, page 1-12. IEEE, (2021)Status-quo Policy Gradient in Multi-Agent Reinforcement Learning., , , , , , and . AAMAS, page 1530-1532. International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS), (2022)Approximate information state for approximate planning and reinforcement learning in partially observed systems., , , and . CoRR, (2020)Robustness and Sample Complexity of Model-Based MARL for General-Sum Markov Games., , and . Dyn. Games Appl., 13 (1): 56-88 (March 2023)Reinforcement Learning in Stationary Mean-field Games., and . AAMAS, page 251-259. International Foundation for Autonomous Agents and Multiagent Systems, (2019)On Controllability of Leader-Follower Dynamics over a Directed Graph., , and . CDC, page 2048-2055. IEEE, (2018)