Upcoming developments in reinforcement learning

The past few sections may have painted a stark outlook for deep learning and reinforcement learning. However, there is no need to feel entirely discouraged; this is, in fact, an exciting time for DL and RL, where many significant advances in research are continuing to shape the field and cause it to evolve at a rapid pace. With increasing availability of computational resources and data, the possibilities of expanding and improving deep learning and reinforcement learning algorithms continue to expand.

 

Addressing the limitations

For one, the issues raised in the preceding section are recognized and acknowledged by the research community. There are several efforts being made to address them. In the work by Pattanaik et. al., not only do the authors demonstrate that current deep reinforcement learning algorithms are susceptible to adversarial attacks, they also propose techniques that can make the same algorithms more robust toward such attacks...