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SocialAI: Benchmarking Socio-Cognitive Abilities in Deep Reinforcement Learning Agents
[article]
2021
arXiv
pre-print
Building embodied autonomous agents capable of participating in social interactions with humans is one of the main challenges in AI. Within the Deep Reinforcement Learning (DRL) field, this objective motivated multiple works on embodied language use. However, current approaches focus on language as a communication tool in very simplified and non-diverse social situations: the "naturalness" of language is reduced to the concept of high vocabulary size and variability. In this paper, we argue
arXiv:2107.00956v3
fatcat:6jyi3eivtfctbl2vl66se2jy3q