RL4Grid 0.2.0.1

An RL Env for optimal dispatching

RL4Grid Environment

    This is a custom reinforcement learning environment, designed for power system optimal dispatch problem.
    
    ## Features
    
    - action: generator active power setpoints
    - networks: IEEE 14, 39, 57, 300 systems and SG126
    
    ## Installation
    
    ### Install via `pip`
    
    You can install the environment package using `pip`:
    
    ```bash
    pip install RL4Grid
    ```
    
    ## Usage Example
    Once installed, you can use the reinforcement learning environment as follows:
    
    ```python
    
    import gym
    import RL4Grid  # Import your environment
    
    # Create the environment
    env = gym.make("MyRL-v0")
    
    # Reset the environment
    env.reset()
    
    # Interact with the environment
    for _ in range(10):
        action = env.action_space.sample()  # Sample a random action
        obs, reward, done, info = env.step(action)  # Take a step
        print(f"Observation: {obs}, Reward: {reward}, Done: {done}")
    
        if done:
            env.reset()
            
    ```
    
    ## Data
    Download data at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi%3A10.7910%2FDVN%2F01JJZY&version=DRAFT#
    
    Extract and put data/ at RL4Grid/RL4Grid/
    
    
    ## Test
    ```bash
    cd RL4Grid/RL4Grid
    python test.py
    ```