15. Conclusion

Understanding Our Reinforcement Learning Trading Agent

This summary provides an overview of the critical components of a reinforcement learning trading agent and their roles within the system.

• Deep Q Network (DQN): A neural network used to predict the value of actions given a state, forming the backbone of the trading agent.
• Reward Functions: Define the returns or penalties received for actions, guiding the agent's learning process.
• Exploration vs. Exploitation: Utilizes DQN with Epsilon greedy strategy to balance trying new actions and using known successful ones.
• Experience Replay: Analyzes past experiences to strengthen learning over time by considering various state-action pairs.
• Training Process: Describes the integration of internal components with external market signals to facilitate effective learning.

The upcoming focus will be on testing, refining, and improving the trading agent's effectiveness through a method known as backtesting, essential in real-world trading scenarios.