This repository provides a set of methods to establish the computational loop of EnergyPlus within a Markov Decision Process (MDP), treating it as a multi-agent environment compatible with RLlib. The main goal is to offer a simple configuration of EnergyPlus as a standard environment for experimentation with Deep Reinforcement Learning.
To install EnergyPlusRL, simply use pip:
pip install eprllib
- Integration of EnergyPlus and RLlib: This package facilitates setting up a Reinforcement Learning environment using EnergyPlus as the base, allowing for experimentation with energy control policies in buildings.
- Simplified Configuration: To use this environment, you simply need to provide a configuration in the form of a dictionary that includes state variables, metrics, actuators (which will also serve as agents in the environment), and other optional features.
- Flexibility and Compatibility: EnergyPlusRL easily integrates with RLlib, a popular framework for Reinforcement Learning, enabling smooth setup and training of control policies for actionable elements in buildings.
- Import eprllib.
- Configure EnvConfig to provide a EnergyPlus model based configuration, specifying the parameters required (see eprllib.Env.EnvConfig).
- Configure RLlib algorithm to train the policy.
- Execute the training using RLlib or Tune.
# Import the libraries needed.
import ray
from ray.tune import register_env
from ray.rllib.algorithms.ppo.ppo import PPOConfig
import eprllib
from eprllib.Env.MultiAgen.EnvConfig import EnvConfig, env_config_to_dic
from eprllib.Env.MultiAgent.EnergyPlusEnv import EnergyPlusEnv_v0
# Configure eprllib.
BuildingModel = EnvConfig()
BuildingModel.generals(
epjson_path=‘path_to_epJSON_file’,
epw_path=‘path_to_EPW_file’,
output_path=‘path_to_output_folder’,
)
BuildingModel.agents(
agents_config = {
‘Thermal Zone: Room1’:{
‘Agent 1 in Room 1’: {
‘ep_actuator_config’: (),
‘thermal_zone’: ‘Thermal Zone: Room 1’,
‘actuator_type’: 3 ,
‘agent_id’: 1,
},
}
)
# Start a Ray server.
ray.init()
# Register the environment.
register_env(name="EPEnv", env_creator=lambda args: EnergyPlusEnv_v0(args))
# Configure the algorith and assign the environment registred.
algo = PPOConfig ( )
algo.environment(
env = "EPEnv",
env_config = env_config_to_dict(BuildingModel)
)
algo.build()
# Train the policy with Tune.
tune.Tuner(
'PPO',
tune_config=tune.TuneConfig(
mode="max",
metric="episode_reward_mean",
),
run_config=air.RunConfig(
stop={"episodes_total": 10},
),
param_space=algo.to_dict(),
).fit()
Contributions are welcome! If you wish to improve this project or add new features, feel free to submit a pull request. Checkout our Code of Conduct and How to Contribute documentation.
MIT License
Copyright (c) 2024 Germán Rodolfo Henderson
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Copyright 2023 Ray Authors
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
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EnergyPlus, Copyright (c) 1996-2024, The Board of Trustees of the University of Illinois, The Regents of the University of California, through Lawrence Berkeley National Laboratory (subject to receipt of any required approvals from the U.S. Dept. of Energy), Oak Ridge National Laboratory, managed by UT-Battelle, Alliance for Sustainable Energy, LLC, and other contributors. All rights reserved.
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