using Reinforcement learning for binary classification

I want to build an agent for binary classification. I have a large dataset with two label (0 and 1). I want to build an agent to predict labels. I build a deep model and now I want to build an agent. I use keras-rl2. but there is a problem: for dqn agent, the fit function has an env argument. I don't know how can I define my problem environment for that. note that my problem has a similarity function that optimize weights for each feature. the agent can also select the best weight for each feature. but the problem is that I don't know how can I define my environment.