IJCAI 2020poster0 citations
Generalized Representation Learning Methods for Deep Reinforcement Learning
Abstract
Deep reinforcement learning (DRL) increases the successful applications of reinforcement learning (RL) techniques but also brings challenges such as low sample efficiency. In this work, I propose generalized representation learning methods to obtain compact state space suitable for RL from a raw observation state. I expect my new methods will increase sample efficiency of RL by understandable representations of state and therefore improve the performance of RL.
Machine Learning: Deep Reinforcement LearningHeuristic Search and Game Playing: Game Playing and Machine LearningMachine Learning: Probabilistic Machine Learning
BibTeX
@inproceedings{ijcai2020p748,
title = {Generalized Representation Learning Methods for Deep Reinforcement Learning},
author = {Zhu, Hanhua},
booktitle = {Proceedings of the Twenty-Ninth International Joint Conference on
Artificial Intelligence, {IJCAI-20}},
publisher = {International Joint Conferences on Artificial Intelligence Organization},
editor = {Christian Bessiere},
pages = {5216--5217},
year = {2020},
month = {7},
note = {Doctoral Consortium},
doi = {10.24963/ijcai.2020/748},
url = {https://doi.org/10.24963/ijcai.2020/748},
}