Entropy-regularized RL (G-learning) vs. IRL (Inverse Reinforcement Learning)

What are the differences between entropy-regularized RL (G-learning) and IRL (Inverse Reinforcement Learning)? and how are they applied to actual problems (besides stand-alone Markov decision processes)?