As a CityLearn user, I want to be able to take advantage of stable baselines3 reliable implementations of RL algorithms to enable me easily evaluate my environment on a diverse set of algorithms and benchmark the performance of the algorithms.
Changes can be made to the environment as long as that the evaluation criteria below are met.
Acceptance Criteria
[ ] Setup works for the RL algorithms that make use of Box gym.space.
[ ] Setup works for n building environment when env.central_agent = True (single agent controls all buildings)
[ ] Setup works for n building environment when env.central_agent = False (independent multi-agent i.e. each building has its own agent and agents do no share information)
[ ] Setup does not disrupt the compatibility of the environment with CityLearn’s RBC, SAC, and MARLISA implementations in citylearn/agents.
[ ] The example.ipynb notebook provides an example implementation of using at least on of the Stable Baselines3 algorithms for n buildings in central and non-central agent scenarios.
As a CityLearn user, I want to be able to take advantage of stable baselines3 reliable implementations of RL algorithms to enable me easily evaluate my environment on a diverse set of algorithms and benchmark the performance of the algorithms.
Changes can be made to the environment as long as that the evaluation criteria below are met.
Acceptance Criteria
References