Meta-learning for wrestling

OpenAI Blog · 8 years ago

Meta-learning agents trained on simulated robot wrestling tasks can quickly adapt to defeat stronger opponents that lack meta-learning capabilities. The meta-learning approach enables agents to adjust their strategy during matches, including adapting when experiencing simulated physical malfunctions. This demonstrates that meta-learning could be useful for robotic systems that need to handle unexpected changes in their environment or body during operation.