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About this paper
Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel
The authors train gradient-based policy updates to use experience from one task in a changing sequence to prepare for the next. They test this meta-learning approach in simulated locomotion and repeated RoboSumo games. Against a Bug opponent, the Bug agent reaches a reported 43.7% win rate, versus 21.2% for PPO tracking, in this few-shot setting.
Key insights
- Meta-learning effectively biases agents for rapid, few-shot adaptation in continuously changing environments.
- Framing nonstationarity as a Markov chain of tasks allows meta-learning to anticipate future changes based on current experience.
- Meta-learned adaptation strategies demonstrate robustness and superior fitness in competitive multi-agent environments, dominating populations over time.
Summary by alphaXiv.
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