Reinforcement Learning Algorithms with Python

IRL

One of the biggest limitations of IL lies in its inability to learn other trajectories to reach a goal, except those learned from the expert. By imitating an expert, the learner is constrained to the range of behaviors of its teacher. They are not aware of the end goal that the expert is trying to reach. Thus, these methods are only useful when there's no intention to perform better than the teacher.

IRL is an RL algorithm, such as IL, that uses an expert to learn. The difference is that IRL uses the expert to learn its reward function. Therefore, instead of copying the demonstrations, as is done in imitation learning, IRL figures out the goal of the expert. Once the reward function is learned, the agent uses it to learn the policy. ...