Deep reinforcement learning for optimal trading with partial information
The paper explores using reinforcement learning to optimize trading when some market parameters are unknown. It proposes three methods to handle this uncertainty: directly encoding hidden states, estimating latent parameter probabilities, or forecasting future signal values. The results show that incorporating probabilistic insights into latent regimes significantly improves trading strategy profitability and robustness in real markets.