2020/01/19 by Daichi Nishio, Nishio, Daichi, Daiki Kuyoshi +5
Computer Science · Mathematics · Neuroscience · #Adversarial Robustness in Machine Learning #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural dynamics and brain function #Reinforcement Learning in Robotics #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2001.06808
openalex publication_date 2020/01/19 · arxiv created 2020/01/31 · arxiv updated 2020/02/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
It is difficult to be able to imitate well in unknown states from a small amount of expert data and sampling data. Supervised learning methods such as Behavioral Cloning do not require sampling data, but usually suffer from distribution shift. The methods based on reinforcement learning, such as inverse reinforcement learning and generative adversarial imitation learning (GAIL), can learn from only a few expert data. However, they often need to interact with the environment. Soft Q imitation learning addressed the problems, and it was shown that it could learn efficiently by combining Behavioral Cloning and soft Q-learning with constant rewards. In order to make this algorithm more robust to distribution shift, we propose Discriminator Soft Actor Critic (DSAC). It uses a reward function based on adversarial inverse reinforcement learning instead of constant rewards. We evaluated it on PyBullet environments with only four expert trajectories.