4 papers
Implicit Repair with Reinforcement Learning in Emergent Communication
Fábio Vital, Alberto Sardinha, Francisco S. Melo
Conversational repair is a mechanism used to detect and resolve miscommunication and misinformation problems when two or more agents interact. One particular and underexplored form…
Distributed Value Decomposition Networks with Networked Agents
Guilherme S. Varela, Alberto Sardinha, Francisco S. Melo
We investigate the problem of distributed training under partial observability, whereby cooperative multi-agent reinforcement learning agents (MARL) maximize the expected cumulativ…
Networked Agents in the Dark: Team Value Learning under Partial Observability
Guilherme S. Varela, Alberto Sardinha, Francisco S. Melo
We propose a novel cooperative multi-agent reinforcement learning (MARL) approach for networked agents. In contrast to previous methods that rely on complete state information or j…
Learning to Perceive in Deep Model-Free Reinforcement Learning
Gonçalo Querido, Alberto Sardinha, Francisco S. Melo
This work proposes a novel model-free Reinforcement Learning (RL) agent that is able to learn how to complete an unknown task having access to only a part of the input observation.…