mlsanityData to 14 Oct 2026

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

arXiv 2209.15626 ↗First posted 30 Sep 2022cs.LGcited by 1

On the map: Offline RL and imitation learning from data → Offline RL algorithms

Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Placed in Offline RL algorithms, one of 13 directions in Offline RL and imitation learning from data. Builds on 12 of its 35 references that are in the map. First posted on arXiv 30 Sep 2022.

Builds on

12 of 35 references.

Closest ancestors · 4

Same direction, newest first

  1. 2021A Minimalist Approach to Offline Reinforcement Learning12 Jun
  2. 2020Provably Good Batch Reinforcement Learning Without Great Exploration16 Jul
  3. 2020Conservative Q-Learning for Offline Reinforcement Learning8 Jun
  4. 2020D4RL: Datasets for Deep Data-Driven Reinforcement Learning15 Apr

Other references · 8

  1. 2017CARLA: An Open Urban Driving Simulatorcited by 2,449
  2. 2018Off-Policy Deep Reinforcement Learning without Explorationcited by 1,241
  3. 2019Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reductioncited by 840
  4. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 715
  5. 2019Relay Policy Learning: Solving Long-Horizon Tasks via Imitation and Reinforcement Learningcited by 384
  6. 2017Flow: A Modular Learning Framework for Mixed Autonomy Trafficcited by 73
  7. 2020CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy Managementcited by 27
  8. 2019Building HVAC Scheduling Using Reinforcement Learning via Neural Network Based Model Approximationcited by 16

Built on by

Cited by year

Citing papers per year: 2024: 1.

1 paper.

  1. 2024SINERGYM -- A virtual testbed for building energy optimization with Reinforcement Learningcited by 5

Similar, not cited

Close in wording, no citation either way

  1. 2025NeoRL-2: Near Real-World Benchmarks for Offline Reinforcement Learning with Extended Realistic Scenarios
  2. 2024D5RL: Diverse Datasets for Data-Driven Deep Reinforcement Learning
  3. 2021Continuous Doubly Constrained Batch Reinforcement Learning
  4. 2022CORL: Research-oriented Deep Offline Reinforcement Learning Library
  5. 2021NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning
  6. 2024Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
  7. 2022A Survey on Model-based Reinforcement Learning
  8. 2020BRPO: Batch Residual Policy Optimization