Imitation Learning over Heterogeneous Agents with Restraining Bolts
A common problem in Reinforcement Learning (RL) is that the reward function is hard to express. This can be overcome by resorting to Inverse Reinforcement Learning (IRL), which consists in first obtaining a reward function from a set of execution traces generated by the expert agent, and then making the learning agent learn the expert's behavior --this is known as Transfer Learning (TL). Typical IRL solutions rely on a numerical representation of the reward function, which raises problems related to the adopted optimization procedures.