Social Reinforcement Learning
- 16:30 24th February 2021 ( week 6, Hilary Term 2021 )MS Teams
Social learning helps humans and animals rapidly adapt to new circumstances, and drives the emergence of complex learned behaviors. This talk focuses on Social Reinforcement Learning, developing new RL algorithms that leverage social learning to improve single-agent learning and generalization, multi-agent coordination, and human-AI interaction. We will demonstrate how a multi-agent technique for Adversarial Environment Generation based on minimax regret can lead to the generation of a complex curriculum of training environments, which improves an agent’s zero-shot transfer to unknown, single-agent test tasks. To improve multi-agent coordination, we give agents an intrinsic motivation to increase their causal influence over the actions of other agents, and show that this leads to the emergence of communication and enhances cooperation. Finally, we propose a novel Offline RL technique for learning from intrinsic social cues during interaction with humans in an open-domain dialog setting. Together, this work argues that Social RL is a valuable approach for developing more general, sophisticated, and human-compatible AI.
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