3.4k citations · 4.7k across the 28 of their papers we have counts for
6 papers · 1 filter
Mastering the Game of Stratego with Model-Free Multiagent Reinforcement Learning
Julien Perolat, Bart de Vylder, Daniel Hennes +31
We introduce DeepNash, an autonomous agent capable of learning to play the imperfect information game Stratego from scratch, up to a human expert level. Stratego is one of the few…
Learning Natural Language Generation from Scratch
Alice Martin Donati, Guillaume Quispe, Charles Ollion +3
This paper introduces TRUncated ReinForcement Learning for Language (TrufLL), an original ap-proach to train conditional language models from scratch by only using reinforcement le…
Countering Language Drift with Seeded Iterated Learning
Yuchen Lu, Soumye Singhal, Florian Strub +2
Pretraining on human corpus and then finetuning in a simulator has become a standard pipeline for training a goal-oriented dialogue agent. Nevertheless, as soon as the agents are f…
Deep Reinforcement Learning and the Deadly Triad
Hado van Hasselt, Yotam Doron, Florian Strub +3
We know from reinforcement learning theory that temporal difference learning can fail in certain cases. Sutton and Barto (2018) identify a deadly triad of function approximation, b…
HoME: a Household Multimodal Environment
Simon Brodeur, Ethan Perez, Ankesh Anand +6
We introduce HoME: a Household Multimodal Environment for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all with…
GuessWhat?! Visual object discovery through multi-modal dialogue
Harm de Vries, Florian Strub, Sarath Chandar +3
We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The goal of the game is to locate an unknow…