<p>If you only have collected data, but no way to interact with the environment then you are in what is called <strong>Offline RL</strong> scenario, which is an active area of research. It has its own pros and cons. The most naive approach can be to use behavioural cloning (so you treat dataset as a normal supervised learning problem and replicate the actions) - the problem is that this assumes data is already coming from good executions. The other way around is to run an RL algorithm with <strong>off policy corrections</strong> since the data is not now coming from your actual policy and thus a policy gradient would be biased etc. Overall - Offline RL is your keyword.</p>
<p>For further reading: <a href="https://arxiv.org/abs/2203.01387" rel="nofollow noreferrer">https://arxiv.org/abs/2203.01387</a></p>
Reinforcement Learning Training on Collected dataset
<p>I am new to Reinforcement learning and I did several examples using the GYM environment. However, I knew and observed that Reinforcement should be trained on the real environment not on collected data like supervised learning, My question here, is this always true? I mean I have a specific dataset which is something like a recommendation system and I want the agent to be trained on it before I publish the agent in the real environment..
Is this possible?</p>