no code implementations • 29 Sep 2021 • Abbas Abdolmaleki, Sandy Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra, Dhruva Tirumala, Arunkumar Byravan, Konstantinos Bousmalis, András György, Csaba Szepesvari, Raia Hadsell, Nicolas Heess, Martin Riedmiller
Many advances that have improved the robustness and efficiency of deep reinforcement learning (RL) algorithms can, in one way or another, be understood as introducing additional objectives or constraints in the policy optimization step.
no code implementations • 15 Jun 2021 • Abbas Abdolmaleki, Sandy H. Huang, Giulia Vezzani, Bobak Shahriari, Jost Tobias Springenberg, Shruti Mishra, Dhruva TB, Arunkumar Byravan, Konstantinos Bousmalis, Andras Gyorgy, Csaba Szepesvari, Raia Hadsell, Nicolas Heess, Martin Riedmiller
We highlight its strengths on standard MO benchmark problems and consider case studies in which we recast offline RL and learning from experts as MO problems.
Invariances to translation, rotation and other spatial transformations are a hallmark of the laws of motion, and have widespread use in the natural sciences to reduce the dimensionality of systems of equations.
Machine learning has gained widespread attention as a powerful tool to identify structure in complex, high-dimensional data.
Soft Condensed Matter