Using Memory-Based Learning to Solve Tasks with State-Action Constraints

no code implementations • 8 Mar 2023 • Mrinal Verghese, Chris Atkeson

Tasks where the set of possible actions depend discontinuously on the state pose a significant challenge for current reinforcement learning algorithms.

reinforcement-learning Reinforcement Learning (RL)

Perspectives on Sim2Real Transfer for Robotics: A Summary of the R:SS 2020 Workshop

no code implementations • 7 Dec 2020 • Sebastian Höfer, Kostas Bekris, Ankur Handa, Juan Camilo Gamboa, Florian Golemo, Melissa Mozifian, Chris Atkeson, Dieter Fox, Ken Goldberg, John Leonard, C. Karen Liu, Jan Peters, Shuran Song, Peter Welinder, Martha White

This report presents the debates, posters, and discussions of the Sim2Real workshop held in conjunction with the 2020 edition of the "Robotics: Science and System" conference.

Random Sampling of States in Dynamic Programming

no code implementations • NeurIPS 2007 • Chris Atkeson, Benjamin Stephens

We combine two threads of research on approximate dynamic programming: random sampling of states and using local trajectory optimizers to globally optimize a policy and associated value function.