Metadata-Version: 2.1
Name: RL4Grid
Version: 0.2.0.1
Summary: An RL Env for optimal dispatching
Home-page: https://github.com/liushaohuai5/RL4Grid
Author: Shaohuai Liu
Author-email: liushaohuai42@gmail.com
License: UNKNOWN
Description: # 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
        ```
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
