Repository for my M.Sc. thesis in RL.
The folder thesis contains the latex code for my thesis. The folder thesis_presentation contains the latex code for my presentation.
A Conf-MDP is a MDP in which the transition function p: (s,a) -> s' is affected by some configurable parameters \omega. The effect of the parameter on the transition function can be known (exact case) or unknown (approximated case).
- Alberto Maria Metelli
- Emanuele Ghelfi emanuele.ghelfi@mail.polimi.it Scholar Github Website Twitter
- Marcello Restelli
@InProceedings{pmlr-v97-metelli19a,
title = {Reinforcement Learning in Configurable Continuous Environments},
author = {Metelli, Alberto Maria and Ghelfi, Emanuele and Restelli, Marcello},
booktitle = {Proceedings of the 36th International Conference on Machine Learning},
pages = {4546--4555},
year = {2019},
editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
volume = {97},
series = {Proceedings of Machine Learning Research},
address = {Long Beach, California, USA},
month = {09--15 Jun},
publisher = {PMLR},`
pdf = {http://proceedings.mlr.press/v97/metelli19a/metelli19a.pdf},
url = {http://proceedings.mlr.press/v97/metelli19a.html},
abstract = {Configurable Markov Decision Processes (Conf-MDPs) have been recently introduced as an extension of the usual MDP model to account for the possibility of configuring the environment to improve the agent’s performance. Currently, there is still no suitable algorithm to solve the learning problem for real-world Conf-MDPs. In this paper, we fill this gap by proposing a trust-region method, Relative Entropy Model Policy Search (REMPS), able to learn both the policy and the MDP configuration in continuous domains without requiring the knowledge of the true model of the environment. After introducing our approach and providing a finite-sample analysis, we empirically evaluate REMPS on both benchmark and realistic environments by comparing our results with those of the gradient methods.}
}