Meta-learning for wrestling
OpenAI Blog
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.
Why it matters
We show that for the task of simulated robot wrestling, a meta-learning agent can learn to quickly defeat a stronger non-meta-learning agent, and also show that the meta-learning agent can adapt to physical malfunction.