Reward-Respecting Subtasks for Model-Based Reinforcement Learning

no code implementations • 7 Feb 2022 • Richard S. Sutton, Marlos C. Machado, G. Zacharias Holland, David Szepesvari, Finbarr Timbers, Brian Tanner, Adam White

Each subtask is solved to produce an option, and then a model of the option is learned and made available to the planning process.

Model-based Reinforcement Learning reinforcement-learning +2

Student of Games: A unified learning algorithm for both perfect and imperfect information games

no code implementations • 6 Dec 2021 • Martin Schmid, Matej Moravcik, Neil Burch, Rudolf Kadlec, Josh Davidson, Kevin Waugh, Nolan Bard, Finbarr Timbers, Marc Lanctot, G. Zacharias Holland, Elnaz Davoodi, Alden Christianson, Michael Bowling

Games have a long history as benchmarks for progress in artificial intelligence.

The Effect of Planning Shape on Dyna-style Planning in High-dimensional State Spaces

no code implementations • 5 Jun 2018 • G. Zacharias Holland, Erin J. Talvitie, Michael Bowling

Dyna is a fundamental approach to model-based reinforcement learning (MBRL) that interleaves planning, acting, and learning in an online setting.

Atari Games Model-based Reinforcement Learning +1

Multi-step Reinforcement Learning: A Unifying Algorithm

no code implementations • 3 Mar 2017 • Kristopher De Asis, J. Fernando Hernandez-Garcia, G. Zacharias Holland, Richard S. Sutton

These methods are often studied in the one-step case, but they can be extended across multiple time steps to achieve better performance.

Q-Learning reinforcement-learning +2