Part 4/9:
At the core of the Stanford research is the application of multi-agent reinforcement learning (MARL), which has traditionally relied on extensive datasets of human interactions for training. This dependency on vast amounts of communication data has posed challenges, limiting AI’s ability to function effectively without previous examples of human behavior.
The authors of this study proposed a novel mechanism to provide a dense reward signal during agent interactions in social deduction games. This approach allows AI agents to enhance their performance without necessitating abundant human data, thereby simplifying the training process.