  Reinforcement learning has been shown to perform a range of complex tasks
through interaction with an environment or collected leveraging experience.
However, many of these approaches presume optimal or near optimal experiences
or the presence of a consistent environment. In this work we propose dual,
advantage-based behavior policy based on counterfactual regret minimization. We
demonstrate the flexibility of this approach and how it can be adapted to
online contexts where the environment is available to collect experiences and a
variety of other contexts. We demonstrate this new algorithm can outperform
several strong baseline models in different contexts based on a range of
continuous environments. Additional ablations provide insights into how our
dual behavior regularized reinforcement learning approach is designed compared
with other plausible modifications and demonstrates its ability to generalize.
