Learning

DEX Navi's AI agent is not a static rule-based system but a dynamic system that continuously learns and improves. Leveraging the strengths of the GAME framework, it enhances its performance with each trade.

Learning from Trading Results: The AI agent receives feedback from the results of its executed trades (e.g., execution price, slippage, fees) and uses this information to optimize future trading strategies. For example, if slippage was significant on a particular DEX, it will lower the priority of that DEX or adjust the order size in subsequent trades.

Adapting to Changes in Market Conditions: The DEX market is constantly changing. Various factors, such as the emergence of new DEXs, trends among liquidity providers, and token price fluctuations, influence the market environment. DEX Navi's AI agent continuously monitors these changes and dynamically adjusts its trading strategy to consistently deliver optimal performance.

Reinforcement Learning: The GAME framework supports reinforcement learning algorithms. Through reinforcement learning, DEX Navi's AI agent is expected to autonomously acquire more sophisticated trading strategies. For example, it can learn through trial and error complex decisions such as which order type to use, when, and on which DEX under specific market conditions.