Transdreamer: Reinforcement learning with transformer world models
The Dreamer agent provides various benefits of Model-Based Reinforcement Learning
(MBRL) such as sample efficiency, reusable knowledge, and safe planning. However, its
world model and policy networks inherit the limitations of recurrent neural networks and thus
an important question is how an MBRL framework can benefit from the recent advances of
transformers and what the challenges are in doing so. In this paper, we propose a
transformer-based MBRL agent, called TransDreamer. We first introduce the Transformer …
(MBRL) such as sample efficiency, reusable knowledge, and safe planning. However, its
world model and policy networks inherit the limitations of recurrent neural networks and thus
an important question is how an MBRL framework can benefit from the recent advances of
transformers and what the challenges are in doing so. In this paper, we propose a
transformer-based MBRL agent, called TransDreamer. We first introduce the Transformer …
[CITATION][C] Transdreamer: Reinforcement learning with transformer world models, 2022
C Chen, YF Wu, J Yoon, S Ahn - URL https://arxiv. org/abs/2202.09481
Showing the best results for this search. See all results
