Learning to Learn with Probabilistic Task Embeddings

To operate successfully in a complex and changing environment, learning agents must be able to acquire new skills quickly. Humans display remarkable skill in this area -- we can learn to recognize a new object from one example, adapt to driving a different car in a matter of minutes, and add a new slang word to our vocabulary after hearing it once. Meta-learning is a promising approach for enabling such capabilities in machines. In this paradigm, the agent adapts to a new task from limited data by leveraging a wealth of experience collected in performing related tasks. For agents that must take actions and collect their own experience, meta-reinforcement learning (meta-RL) holds the promise of enabling fast adaptation to new scenarios.