Emergent Communication for Deep Reinforcement Agents
| Componente | Categoria |
|---|---|
| Luca Iocchi | Aggiungi Tutor di riferimento (Professore o Ricercatore afferente allo stesso Dipartimento del Proponente) |
In this proposal, the candidate introduces the idea to perform his work in a hot research topic in the field of Machine Learning and Natural language processing, such as Emergent communication. The broad aim of the project is to let artificial agents interact with each other and develop a language that shows similar syntactic measures to the human one, e.g., compositionality.
Identifying a solid learning framework is the first critical step to give agents the capability of building up their own communication. Therefore, we select the EGG environment which has been used in the literature as our primary environment and extend it. The latter implements the referential game setting, first introduced by David Lewis, which is considered a stable environment to study language emergent for a wide variety of agents.
The second critical step is finding a set of evaluation metrics to understand what kind of language is being developed by the artificial agents. We leverage recent works that focus their attention on implementing such metrics and analyze our results accordingly.