fd5c905bcd8c3348ad1b35d7231ee2b1-Reviews.html

This paper is published in the context of making Learning from Demonstration more robust when a limited number of demonstrations are available. Many of the low level trajectory learning LfD approaches suffer from fragile policies. This paper proposes to use Reinforcement learning to overcome this limitation. This paper falls squarely in the LfD field and does not tackle Inverse reinforcement learning, i.e. the reward function is assumed to be known to the agent rather than inferred by demonstration. One work with a very similar flavor is that of Smart, W. and Kaelbling, L.P. "Effective Reinfrocement Learning for Mobile Robots" ICRA 2002.