Welcome to the realm of Sinergym! This innovative tool allows you to wrap simulation engines like EnergyPlus with seamless integration into the Gymnasium API. With the goal of enhancing building control using deep reinforcement learning (DRL) techniques, Sinergym opens doors to futuristic energy management solutions. In this guide, we’ll take you step-by-step through the setup and usage of Sinergym.

Prerequisites

Before you dive in, ensure you have the following:

• Python 3.12 or earlier
• Basic knowledge of Python programming
• Familiarity with Gymnasium API

Installation Steps

To install Sinergym, refer to the detailed instructions provided in our INSTALL.md. The general installation process involves cloning the repository and setting up dependencies.

Using Sinergym

Once Sinergym is installed, you can start using it for your deep RL endeavors. Below is an analogy to help you understand how to set up your environment:

Think of building a simulation environment like setting up a kitchen. You have various ingredients (simulation parameters) that can be mixed and matched to create delicious dishes (simulation scenarios). In our example, the ingredients are the different Gym spaces you can manipulate!

Example Code

Here’s a simple example of how to create and run a simulation environment:

python
import gymnasium as gym
import sinergym

# Create the environment
env = gym.make("Eplus-datacenter-mixed-continuous-stochastic-v1")

# Initialize the episode
obs, info = env.reset()
truncated = terminated = False
R = 0.0

while not (terminated or truncated):
    a = env.action_space.sample()  # Random action selection
    obs, reward, terminated, truncated, info = env.step(a)  # Get new observation and reward
    R += reward
    
print("Total reward for the episode: %.4f" % R)
env.close()

This snippet illustrates how to create the environment, reset it, run an episode, and print the total reward received. The environment managed inside the `gym` makes it easier to handle simulations just like organizing ingredients in a kitchen!

Troubleshooting

If you encounter issues, consider the following troubleshooting tips:

• Ensure that you have compatible Python versions as Pytype is currently disabled for version 3.12.
• Check if all required dependencies are installed. Missing libraries might lead to errors.
• If the environment doesn’t initialize, verify that it has been correctly specified in `gym.make`.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Further Information

For a complete list of available environments, check out our documentation. Sinergym is continually evolving, and we encourage community feedback and contributions!

Explore More

Before you embark on using Sinergym in your projects, don’t forget to explore the extensive documentation and examples found in our documentation. Also, for those looking to deploy Sinergym on Google Cloud, further details can be located here.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.