pythonpaperrlmodel-basedafter-work
Introducing Model-based Reinforcement Learning for Predictions and Control for Limit Order Books for after-work quants
Wei, Wang, Mangu and Decker (2019) train a CNN autoencoder, RNN-MDN transition and reward head on HKEX LOB+prints, then PG/A2C/Double DQN entirely inside the world model. Policy transfers back to replay. Plain-language intro. Not investment advice.