Michael L. Littman
4 papers in the PaperMetrix corpus
Papers by this author
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Successor Features Combine Elements of Model-Free and Model-based Reinforcement Learning
2019 · arXiv (Cornell University)
A key question in reinforcement learning is how an intelligent agent can generalize knowledge across different inputs. By generalizing across different inputs, information learned for one input can be immediately reused for improving predictions for …
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Lipschitz Lifelong Reinforcement Learning
2021 · Proceedings of the AAAI Conference on Artificial Intelligence
We consider the problem of knowledge transfer when an agent is facing a series of Reinforcement Learning (RL) tasks. We introduce a novel metric between Markov Decision Processes and establish that close MDPs have close …
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People Teach with Rewards and Punishments as Communication not Reinforcements
2018
Carrots and sticks motivate behavior, and people can teach new behaviors to other organisms, such as children or non-human animals, by tapping into their reward learning mechanisms. But how people teach with reward and punishment …
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Enabling End Users to Program Robots Using Reinforcement Learning
2025
Reinforcement learning (RL) is a powerful learning technique in robotics, where people can specify rewards that robots learn how to maximize through a process of trialanderror. Despite the numerous advantages of RL to robot programming, …