This paper investigates the effectiveness of transfer learning techniques for accelerating the training of deep reinforcement learning (RL) agents in the foreign exchange (Forex) market. Specifically, the transfer of policies learned on the EUR/USD currency pair to the GBP/USD pair is the focus. Four transfer learning approaches are systematically compared: zero-shot transfer, full fine-tuning, partial fine-tuning, and reward-function transfer. A modular pipeline was developed, incorporating sinusoidal and trend/momentum-based market features, stepwise agent-specific metrics, and deep Q-network (DQN) architectures from the Stable Baselines 3 framework. Agent performance is evaluated through cumulative reward and Sharpe ratio metrics.
Experimental results demonstrate that partial fine-tuning accelerates initial learning by preserving generic market features acquired from the EUR/USD pair. However, our results indicate that directly training on the target currency pair yields superior ultimate performance, highlighting the nuanced limitations and potential benefits of cross-currency transfer learning in algorithmic trading.