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Found 2 papers, 1 papers with code
Active Inverse Learning in Stackelberg Trajectory Games
We formulate an inverse learning problem in a Stackelberg game between a leader and a follower, where each player's action is the trajectory of a dynamical system.
Enabling Efficient, Reliable Real-World Reinforcement Learning with Approximate Physics-Based Models
We focus on developing efficient and reliable policy optimization strategies for robot learning with real-world data.
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