Decoupling State Representation Methods from Reinforcement Learning in Car Racing

Juan Montoya, Imant Daunhawer, Julia Vogt, Marco Wiering

Abstract

In the quest for efficient and robust learning methods, combining unsupervised state representation learning and reinforcement learning (RL) could offer advantages for scaling RL algorithms by providing the models with a useful inductive bias. For achieving this, an encoder is trained in an unsupervised manner with two state representation methods, a variational autoencoder and a contrastive estimator. The learned features are then fed to the actor-critic RL algorithm Proximal Policy Optimization (PPO) to learn a policy for playing Open AI’s car racing environment. Hence, such procedure permits to decouple state representations from RL-controllers. For the integration of RL with unsupervised learning, we explore various designs for variational autoencoders and contrastive learning. The proposed method is compared to a deep network trained directly on pixel inputs with PPO. The results show that the proposed method performs slightly worse than directly learning from pixel inputs; however, it has a more stable learning curve, a substantial reduction of the buffer size, and requires optimizing 88% fewer parameters. These results indicate that the use of pre-trained state representations has several benefits for solving RL tasks.

Download


Paper Citation


in Harvard Style

Montoya J., Daunhawer I., Vogt J. and Wiering M. (2021). Decoupling State Representation Methods from Reinforcement Learning in Car Racing.In Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-484-8, pages 752-759. DOI: 10.5220/0010237507520759


in Bibtex Style

@conference{icaart21,
author={Juan Montoya and Imant Daunhawer and Julia Vogt and Marco Wiering},
title={Decoupling State Representation Methods from Reinforcement Learning in Car Racing},
booktitle={Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2021},
pages={752-759},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010237507520759},
isbn={978-989-758-484-8},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Decoupling State Representation Methods from Reinforcement Learning in Car Racing
SN - 978-989-758-484-8
AU - Montoya J.
AU - Daunhawer I.
AU - Vogt J.
AU - Wiering M.
PY - 2021
SP - 752
EP - 759
DO - 10.5220/0010237507520759