Hi Ran,
In the context of customizing a step function for a reinforcement learning (RL) environment in MATLAB, while managing the step index (or hour, in your case) directly within the step function isn't directly supported through a built-in method, you can effectively manage this by incorporating the step index as part of the environment's state. This approach is more aligned with the principles of reinforcement learning, where the agent's perception of the environment (including the time step within an episode) should ideally be part of the state representation.
Here’s how you can implement this:
1. State Representation
Extend your environment’s state to include the current step index. For instance, if your original state is a vector of sensor readings, you can add another element to this vector to represent the current step index (or hour).
2. Initialization
When an episode starts (in your reset function), initialize the step index to 1 (or 0, depending on your preference) as part of the initial state.
3. Updating the Step Index
Each time your step function is called, increment the step index within the state. This requires extracting the step index from the state, incrementing it, and then updating the state with the new step index. You will also need to handle the transition between episodes, ensuring that the step index is reset appropriately.
4. Considerations
- Adding the step index to the state increases its dimensionality. Ensure that this does not excessively complicate the learning process for your agent.
- You might need to normalize the step index when adding it to the state to ensure it's on a similar scale as other state variables, which can help improve learning efficiency.
This method should provide a clean and integrated way to manage time-dependent constraints within your RL environment without resorting to less desirable practices like global variables.
