no code implementations • 9 Mar 2022 • Christopher Grimm, Naveen Verma
Deep learning training involves a large number of operations, which are dominated by high dimensionality Matrix-Vector Multiplies (MVMs).
1 code implementation • NeurIPS 2021 • Christopher Grimm, André Barreto, Gregory Farquhar, David Silver, Satinder Singh
The value-equivalence (VE) principle proposes a simple answer to this question: a model should capture the aspects of the environment that are relevant for value-based planning.
Model-based Reinforcement Learning Reinforcement Learning (RL)
no code implementations • 23 Dec 2020 • Christopher Grimm, Tai Fei, Ernst Warsitz, Ridha Farhoud, Tobias Breddermann, Reinhold Haeb-Umbach
As the warping operation relies on accurate scene flow estimation, we further propose a novel scene flow estimation algorithm which exploits information from camera, lidar and radar sensors.
no code implementations • NeurIPS 2020 • Christopher Grimm, André Barreto, Satinder Singh, David Silver
As our main contribution, we introduce the principle of value equivalence: two models are value equivalent with respect to a set of functions and policies if they yield the same Bellman updates.
Model-based Reinforcement Learning reinforcement-learning +2
no code implementations • 25 Nov 2019 • Christopher Grimm, Irina Higgins, Andre Barreto, Denis Teplyashin, Markus Wulfmeier, Tim Hertweck, Raia Hadsell, Satinder Singh
This is in contrast to the state-of-the-art reinforcement learning agents, which typically start learning each new task from scratch and struggle with knowledge transfer.
no code implementations • 24 Jan 2019 • Christopher Grimm, Satinder Singh
We present a novel method for learning a set of disentangled reward functions that sum to the original environment reward and are constrained to be independently obtainable.
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.
1 code implementation • 26 Oct 2017 • Yuhang Song, Christopher Grimm, Xianming Wang, Michael L. Littman
We examine the problem of learning mappings from state to state, suitable for use in a model-based reinforcement-learning setting, that simultaneously generalize to novel states and can capture stochastic transitions.
Model-based Reinforcement Learning reinforcement-learning +1
no code implementations • 2 Oct 2017 • Melrose Roderick, Christopher Grimm, Stefanie Tellex
We examine the problem of learning and planning on high-dimensional domains with long horizons and sparse rewards.
no code implementations • 19 Sep 2017 • Christopher Grimm, Yuhang Song, Michael L. Littman
Generative adversarial networks (GANs) are an exciting alternative to algorithms for solving density estimation problems---using data to assess how likely samples are to be drawn from the same distribution.
no code implementations • ICLR 2018 • Christopher Grimm, Dilip Arumugam, Siddharth Karamcheti, David Abel, Lawson L. S. Wong, Michael L. Littman
Deep neural networks are able to solve tasks across a variety of domains and modalities of data.