I have a scheduling problem that I am trying to solve with RL (if you are interested in more details you can read about it here Reinforcement learning applicable to a scheduling problem?).
I have created an own environment (OpenAI-Gym) and I have trained the model for one specific day of the simulation. So I have 288 timesteps for one day (1 for each 5 minutes) and the simulation last until the end of the day. So the agent needs to make 288 decisions when having 1 control variable.
Now my question is whether it is possible to successively train an RL agent on the same environment for different days? The environment and reward function will stay the same but the input data will change as every day has different input data (temperature, heat demand, electricity price etc.). So I would like to train an agent for one day and then tell the agent to train on another day but not forget everything it has learned during the training of the first day. Thus I can make sure that the agent is not overfitting to one special input data but also has the ability to generalize and thus be applicable for different days.
Do you know if and how I can do this?
Reminder: As I still don't know how to tackle this problem I would like to remind you on this question. Can someone give me some advice on how to do this (I already got an answer but there are many questions open to me and the author does not seem to reply any more)?