Reinforcement Learning

Definition ∞ Reinforcement learning is a type of machine learning where an agent learns to make decisions by performing actions in an environment to maximize a cumulative reward. The agent receives feedback in the form of rewards or penalties, adjusting its strategy over time. This learning process is data-driven and adaptive.
Context ∞ The application of reinforcement learning in the cryptocurrency domain is an area of growing interest, particularly for algorithmic trading, portfolio optimization, and smart contract security analysis. Researchers are investigating its potential to develop sophisticated trading bots and detect anomalies in network behavior. The ability of reinforcement learning models to adapt to dynamic market conditions makes them a promising tool for navigating the complexities of digital asset markets.