Behavior Regularized Offline Reinforcement Learning

Understand

In reinforcement learning (RL) research, it is common to assume access to direct online interactions with the environment.

• However in many real-world applications, access to the environment is limited to a fixed offline dataset of logged experience.
• In such settings, standard RL algorithms have been shown to diverge or otherwise yield poor performance.
• Accordingly, recent work has suggested a number of remedies to these issues.