Mitigating Planner Overfitting in Model-Based Reinforcement Learning

no code implementations • 3 Dec 2018 • Dilip Arumugam, David Abel, Kavosh Asadi, Nakul Gopalan, Christopher Grimm, Jun Ki Lee, Lucas Lehnert, Michael L. Littman

An agent with an inaccurate model of its environment faces a difficult choice: it can ignore the errors in its model and act in the real world in whatever way it determines is optimal with respect to its model.

Model-based Reinforcement Learning Position +2

Measuring and Characterizing Generalization in Deep Reinforcement Learning

no code implementations • 7 Dec 2018 • Sam Witty, Jun Ki Lee, Emma Tosch, Akanksha Atrey, Michael Littman, David Jensen

We re-examine what is meant by generalization in RL, and propose several definitions based on an agent's performance in on-policy, off-policy, and unreachable states.

reinforcement-learning Reinforcement Learning (RL) +1

Deep Reinforcement Learning from Policy-Dependent Human Feedback

no code implementations • 12 Feb 2019 • Dilip Arumugam, Jun Ki Lee, Sophie Saskin, Michael L. Littman

To widen their accessibility and increase their utility, intelligent agents must be able to learn complex behaviors as specified by (non-expert) human users.

reinforcement-learning Reinforcement Learning (RL)

Stackelberg Punishment and Bully-Proofing Autonomous Vehicles

no code implementations • 23 Aug 2019 • Matt Cooper, Jun Ki Lee, Jacob Beck, Joshua D. Fishman, Michael Gillett, Zoë Papakipos, Aaron Zhang, Jerome Ramos, Aansh Shah, Michael L. Littman

This idea generalizes the concept of a Stackelberg equilibrium.

Autonomous Vehicles