Learning from Demonstration without Demonstrations
Tom Blau,Philippe Morere,Gilad Francis,Tom Blau,Philippe Morere,Gilad Francis
State-of-the-art reinforcement learning (RL) algorithms suffer from high sample complexity, particularly in the sparse reward case. A popular strategy for mitigating this problem is to learn control policies by imitating a set of expert demonstrations. The drawback of such approaches is that an expert needs to produce demonstrations, which may be costly in practice. To address this shortcoming, we...
