I've looked into policy gradient RL the last few months. As I find the topic quite interesting, I've been readings lots of papers about it. My aim is to write my master thesis in Maths about it. I already started out, the preliminary title being "Techniques for variance reduction in policy gradient reinforcement learning". Of course, I can sum up latest results, but a Master thesis' aim should be to create sth. new, or apply sth. to a new setting. Does anybody have an idea for a nice application? It was my idea to write the thesis in ML. My professor is not that much into ML but is happy to advise and evaluate the thesis.

Advice highly appreciated!


You can look into applying on-policy learning in environments generated via GAN architectures. There’s work on “imagination augmented agents”, where environment information from rollouts are passed into a policy learning network.

https://arxiv.org/abs/1802.03006 This work unrolls the model in latent space, but is in the same vein of passing environment information to a policy network via generative model.

https://arxiv.org/abs/1804.00379 Apparently the search space for forward rollouts in latent space are very large, and this work on backtracking in rollouts attempts to manage this complexity.

Sources for info: mailing lists, friends, colleagues

Good luck with your thesis!

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