ADGEfficiency / Energy Py
Licence: mit
Reinforcement learning for energy systems
Stars: ✭ 148
Programming Languages
python
139335 projects - #7 most used programming language
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energy-py
energypy is a framework for running reinforcement learning experiments on energy environments.
energypy is built and maintained by Adam Green - [email protected].
Installation
$ git clone https://github.com/ADGEfficiency/energy-py
$ pip install --ignore-installed -r requirements.txt
$ python setup.py install
Running experiments
energy-py has a high level API to run a specific run of an experiment from a yaml config file:
$ energypy-experiment energypy/examples/example_config.yaml battery
An example config file (energypy/examples/example_config.yaml):
expt:
name: example
battery: &defaults
total_steps: 10000
env:
env_id: battery
dataset: example
agent:
agent_id: random
Results (log files for each episode & experiment summaries) are placed into a folder in the users $HOME. The progress of an experiment can be watched with TensorBoard by running a server looking at this results folder:
$ tensorboard --logdir='~/energy-py-results'
Low level API
energypy provides the familiar gym style low-level API for agent and environment initialization and interactions:
import energypy
env = energypy.make_env(env_id='battery')
agent = energypy.make_agent(
agent_id='dqn',
env=env,
total_steps=10000
)
observation = env.reset()
while not done:
action = agent.act(observation)
next_observation, reward, done, info = env.step(action)
training_info = agent.learn()
observation = next_observation
Library
energy-py environments follow the design of OpenAI gym. energy-py also wraps some classic gym environments such as CartPole, Pendulum and MountainCar.
energy-py currently implements:
- naive agents
- DQN agent
- Battery storage environment
- Demand side flexibility environment
- Wrappers around the OpenAI gym CartPole, Pendulum and MountainCar environments
Further reading
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