Investigating the role of model-based learning in exploration and transfer

no code implementations • 8 Feb 2023 • Jacob Walker, Eszter Vértes, Yazhe Li, Gabriel Dulac-Arnold, Ankesh Anand, Théophane Weber, Jessica B. Hamrick

Our results show that intrinsic exploration combined with environment models present a viable direction towards agents that are self-supervised and able to generalize to novel reward functions.

Transfer Learning

Active Acquisition for Multimodal Temporal Data: A Challenging Decision-Making Task

no code implementations • 9 Nov 2022 • Jannik Kossen, Cătălina Cangea, Eszter Vértes, Andrew Jaegle, Viorica Patraucean, Ira Ktena, Nenad Tomasev, Danielle Belgrave

We introduce a challenging decision-making task that we call active acquisition for multimodal temporal data (A2MT).

Decision Making Informativeness

Model-Value Inconsistency as a Signal for Epistemic Uncertainty

no code implementations • 8 Dec 2021 • Angelos Filos, Eszter Vértes, Zita Marinho, Gregory Farquhar, Diana Borsa, Abram Friesen, Feryal Behbahani, Tom Schaul, André Barreto, Simon Osindero

Unlike prior work which estimates uncertainty by training an ensemble of many models and/or value functions, this approach requires only the single model and value function which are already being learned in most model-based reinforcement learning algorithms.

Model-based Reinforcement Learning Rolling Shutter Correction

Procedural Generalization by Planning with Self-Supervised World Models

no code implementations • ICLR 2022 • Ankesh Anand, Jacob Walker, Yazhe Li, Eszter Vértes, Julian Schrittwieser, Sherjil Ozair, Théophane Weber, Jessica B. Hamrick

One of the key promises of model-based reinforcement learning is the ability to generalize using an internal model of the world to make predictions in novel environments and tasks.

Benchmarking Meta-Learning +2