Implements Q-learning algorithms combined with recurrent neural networks to create an adaptive trading agent. The system learns optimal portfolio allocation strategies through interaction with market data, using temporal information from RNNs to capture sequential patterns in financial time series. It focuses on long-term cumulative reward optimization rather than instantaneous gains.