984 citations · 3.3k across the 41 of their papers we have counts for
7 papers · 1 filter
Feature-Attending Recurrent Modules for Generalization in Reinforcement Learning
Wilka Carvalho, Andrew Lampinen, Kyriacos Nikiforou +2
Many important tasks are defined in terms of object. To generalize across these tasks, a reinforcement learning (RL) agent needs to exploit the structure that the objects induce. P…
Tell me why! Explanations support learning relational and causal structure
Andrew K. Lampinen, Nicholas A. Roy, Ishita Dasgupta +8
Inferring the abstract relational and causal structure of the world is a major challenge for reinforcement-learning (RL) agents. For humans, language--particularly in the form of e…
Dynamical crossover behavior in the relaxation of quenched quantum many-body systems
Aamir Ahmad Makki, Souvik Bandyopadhyay, Somnath Maity +1
A crossover between different power-law relaxation behaviors of many-body periodically driven integrable systems has come to light in recent years. We demonstrate using integrable…
BEAMetrics: A Benchmark for Language Generation Evaluation Evaluation
Thomas Scialom, Felix Hill
Natural language processing (NLP) systems are increasingly trained to generate open-ended text rather than classifying between responses. This makes research on evaluation metrics…
Multimodal Few-Shot Learning with Frozen Language Models
Maria Tsimpoukelli, Jacob Menick, Serkan Cabi +3
When trained at sufficient scale, auto-regressive language models exhibit the notable ability to learn a new language task after being prompted with just a few examples. Here, we p…
Towards mental time travel: a hierarchical memory for reinforcement learning agents
Andrew Kyle Lampinen, Stephanie C. Y. Chan, Andrea Banino +1
Reinforcement learning agents often forget details of the past, especially after delays or distractor tasks. Agents with common memory architectures struggle to recall and integrat…
Imitating Interactive Intelligence
Josh Abramson, Arun Ahuja, Iain Barr +26
A common vision from science fiction is that robots will one day inhabit our physical spaces, sense the world as we do, assist our physical labours, and communicate with us through…