Deep Bayesian Reinforcement Learning in Changing Environments

Deep Reinforcement Learning (DRL) worked well in a wide range of games with a fixed environment, such as Go and Starcraft. But most real-world environments change over time and are influenced by random factors such as weather. So, the nonstationarity of the environment in DRL requires more attention.

This PhD research sheds light on Reinforcement Learning in changing environments with Bayesian approach since Bayesian DRL can deal with environments with uncertainty. All we need is to treat the environmental changes as uncertainty. In general, I plan to pursue four research directions to help fast learning in changing environments with Bayesian RL.

I first investigate utilising prior experience to identify current environment status in a changing environment setting and then learn strategies. Then I suggest using the meta-learning tricks to facilitate adaptation to new environments in Bayesian RL.

Finally, I propose less conservative Robust RL and more efficient Safe RL in changing environments with a Bayesian approach.

These Bayesian RL directions can contribute to an efficient, safe, and robust deep Bayesian RL in changing environments.