How can I build a deep reinforcement learning model that can be trained with multiple time series datasets

Question

I built a DRL model to trade stocks in the financial market but the number of observations is relatively small and I would like to increase it by training the same model with stocks from several different companies. My problem is that I don't know what is the correct way to do this since the price series is a time series. Someone to enlighten me? I have read articles that show that this is possible but none that say how.