@inproceedings{
kim2023numerical,
title={Numerical Goal-based Transformers for Practical Conditions},
author={Seonghyun Kim and Samyeul Noh and Ingook Jang},
booktitle={NeurIPS 2023 Workshop on Goal-Conditioned Reinforcement Learning},
year={2023},
url={https://openreview.net/forum?id=zhCq4KQsYS}
}
Goal-conditioned reinforcement learning (GCRL) studies aim to apply trained agents in realistic environments. In particular, offline reinforcement learning is being studied as a way to reduce the cost of online interactions in GCRL. One such method is Decision Transformer (DT), which utilizes a numerical goal called "return-to-go" for superior performance. Since DT assumes an idealized environment, such as perfect knowledge of rewards, it is necessary to study an improved approach for real-world applications. In this work, we present various attempts and results for numerical goal-based transformers to operate under practical conditions.
Numerical Goal-based Transformers for Practical Conditions
Seonghyun Kim Samyeul Noh Ingook Jang