This paper presents a novel approach to financial trading by framing it as a game and applying deep reinforcement learning techniques. The authors develop a Markov Decision Process model specifically designed for trading tasks and implement it using a modified Deep Recurrent Q-Network algorithm. Key innovations include an action augmentation technique that provides feedback signals for all possible actions, allowing the agent to use greedy policies instead of traditional epsilon-greedy exploration methods, which proves particularly effective in financial market contexts.

The methodology incorporates several practical modifications to standard deep reinforcement learning approaches, including a substantially smaller replay memory (hundreds rather than millions of entries) and longer sequence sampling for recurrent neural network training. These adaptations not only improve performance in financial trading scenarios but also reduce computational requirements, enabling more efficient online learning. The complete algorithm is validated on spot foreign exchange markets, demonstrating the practical viability of this approach for developing automated trading systems that can potentially generate consistent profits from financial markets.