Towards Real-world Reinforcement Learning for Building Control
The project aims to develop enabling methods for practical deployment of reinforcement learning for building control, such as:
- Initialize a policy with historical data through imitation learning
- Estimate a policy’s performance without running it on the actual system via off-policy evaluation
- Learn on the real buildings with limited samples through model-based RL.