I have a functional LSTM model that works with an acceptable performance. How can I now convert this supervised model to a reinforcement learning model for improving the performane? Is there any example about how to transform a supervised model to a reinforcement learning model?
Details: I have a multi-input multi-output system (since I can not share the actual problem lets assume weather forcast as example) where I need to predict the output (e.g temperature, wind speed, etc) in real time. I have a large dataset and I trianed a supervised learning model that can do the predictions pretty well in real time.
The problem is that sometimes there is big deviation between predictions and actual values. This means that probably there was a new trend in the inputs that has never been in the data sets. For such cases, I would like to gradually enhance my model predictions.
Does it mean that I have to train again my model with the old dataset and the new data or I can simply have a RL mechansim to gradually improve the model?