I'm trying to implement OpenAI Gym environment (for reinforcement learning training) with EnergyPlus building environment. I recently made an effort to achieve this by connecting Python with EnergyPlus through Python API. Currently, my understanding is that E+ simulation is run with few lines of code as below
ARGS = [
'--weather',
weather_file_path,
'--output-directory',
output_dir,
'--readvars',
idf_file_path
]
api = EnergyPlusAPI()
state = api.state_manager.new_state()
api.runtime.run_energyplus(state, ARGS)
and the only way I can interact with each timestep is through callback functions provided by Runtime API.
OpenAI Gym environment requires us to implement a "step" function in a class which will be repeatedly called during RL model training. A pseudo-code of what flow inside a step function with EnergyPlus Python API should look like is shown below
def step(self, action)
set_eplus_actuators_at_start_timestep(action)
new_state = get_eplus_sensor_values_at_end_timestep(.....)
reward = calculate_reward_or_penalty(new_state)
return new_state, reward
However, since each action towards EnergyPlus running simulation can only be done through callback functions, the step function above cannot be achieved through typical single-process programming.
Any suggestion on how to solve this?
