Reinforcement Learning in Trading

Reinforcement learning is a subset of machine learning where an agent learns to make optimal trading decisions by interacting with a simulated market environment to maximize cumulative rewards. The agent receives feedback in the form of profits or losses based on its actions, such as entering or exiting an options position.

Through iterative trial and error, the agent refines its strategy to navigate complex dynamics like slippage, transaction costs, and changing market regimes. This approach is highly effective for developing autonomous market-making bots that must balance inventory risk with profitability in decentralized exchanges.

By continuously updating its policy based on market feedback, the agent adapts to the evolving nature of crypto liquidity.