Demonstration Conditioned Reinforcement Learning for Few-shot Imitation

This paper introduces a novel approach for few-shot imitation learning through demonstration conditioned reinforcement learning. We develop algorithms that enable agents to quickly adapt to new tasks using only a few demonstrations while maintaining robust performance. Our method combines the strengths of imitation learning and reinforcement learning to achieve efficient skill transfer in complex environments.

Conference paper

International Conference on Machine Learning