I am trying to create a chatbot whose dialogue policy model will be trained through reinforcement learning. Dialogue Policy is responsible for selecting the action to take based on the given state of the conversation.
All implementations I see for RL are trained from an environment taken from Gym or created manually. These environments provide the next state, rewards etc to the model based on which it is trained.
Since I am creating a dialogue policy model which will be trained through real user conversations, I cannot provide a "pre-defined" environment which can provide the states and rewards. I am planning to train it myself by talking to it and providing rewards and next state (which I think is called interactive learning).
But I was not able to find any implementations, tutorials or articles on interactive learning. I am not able to figure out how to create such a model, how to take care of the episodes, sessions etc. This will be a continuous learning that will go on for months maybe. I have to save the model each day and continue training the next day by loading the model from that same state.
Can anyone guide me in the correct direction on how to approach this? Any githubs links, articles, tutorials of such implementations will be highly appreciated. I am aware this question seems too broad, but some hints will be very helpful for a newbie like me.