REVIEW 3 cited by
Learning to Play Guess Who? and Inventing a Grounded Language as a Consequence
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Signed reviews
read the original abstract
Acquiring your first language is an incredible feat and not easily duplicated. Learning to communicate using nothing but a few pictureless books, a corpus, would likely be impossible even for humans. Nevertheless, this is the dominating approach in most natural language processing today. As an alternative, we propose the use of situated interactions between agents as a driving force for communication, and the framework of Deep Recurrent Q-Networks for evolving a shared language grounded in the provided environment. We task the agents with interactive image search in the form of the game Guess Who?. The images from the game provide a non trivial environment for the agents to discuss and a natural grounding for the concepts they decide to encode in their communication. Our experiments show that the agents learn not only to encode physical concepts in their words, i.e. grounding, but also that the agents learn to hold a multi-step dialogue remembering the state of the dialogue from step to step.
Forward citations
Cited by 3 Pith papers
-
Drawing with Strangers: Population Scaling Drives Zero-Shot Mutual Intelligibility in Emergent Sketching
Scaling population size during training of emergent sketching agents increases zero-shot mutual intelligibility between independent groups by raising in-group variation and driving perceptual grounding.
-
Mastering emergent language: learning to guide in simulated navigation
A Guide agent trained with a two-token discrete bottleneck learns an emergent guidance language that speeds up a new agent's navigation learning in BabyAI and can be partially reverse-engineered into action commands.
-
A Review of Cooperative Multi-Agent Deep Reinforcement Learning
A review that categorizes cooperative multi-agent deep RL into independent learners, observable critics, value factorization, consensus, and communication, with errors in the taxonomy and references.
Discussion (0). Continue with ORCID to comment.