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SWIRL: A sequential windowed inverse reinforcement learning algorithm for robot tasks with delayed rewards
2018
The international journal of robotics research
Inverse Reinforcement Learning (IRL) allows a robot to generalize from demonstrations to previously unseen scenarios by learning the demonstrator's reward function. However, in multi-step tasks, the learned rewards might be delayed and hard to directly optimize. We present Sequential Windowed Inverse Reinforcement Learning (SWIRL), a three-phase algorithm that partitions a complex task into shorter-horizon subtasks based on Switched Linear Dynamical transitions that occur consistently across
doi:10.1177/0278364918784350
fatcat:ze2skzbkfbek5fntflror45ibq