Pinqian Dong

Adversarial Inverse Reinforcement Learning With Self-Attention Dynamics Model

In many real-world applications where specifying a proper reward function is difficult, it is desirable to learn policies from expert demonstrations. Adversarial Inverse Reinforcement Learning (AIRL) is one of the most common approaches for learning from demonstrations. However, due to the stochasti