B2RL: An open-source Dataset for Building Batch Reinforcement Learning

• URL: http://arxiv.org/abs/2209.15626v1
• Date: Fri, 30 Sep 2022 17:54:42 GMT
• Title: B2RL: An open-source Dataset for Building Batch Reinforcement Learning
• Authors: Hsin-Yu Liu (1), Xiaohan Fu (1), Bharathan Balaji (2), Rajesh Gupta (1), and Dezhi Hong (2) ((1) University of California, San Diego, (2) Amazon)
• Abstract summary: Batch reinforcement learning (BRL) is an emerging research area in the RL community. We are the first to open-source building datasets for the purpose of BRL learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Batch reinforcement learning (BRL) is an emerging research area in the RL community. It learns exclusively from static datasets (i.e. replay buffers) without interaction with the environment. In the offline settings, existing replay experiences are used as prior knowledge for BRL models to find the optimal policy. Thus, generating replay buffers is crucial for BRL model benchmark. In our B2RL (Building Batch RL) dataset, we collected real-world data from our building management systems, as well as buffers generated by several behavioral policies in simulation environments. We believe it could help building experts on BRL research. To the best of our knowledge, we are the first to open-source building datasets for the purpose of BRL learning.

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