Distributional RL (Reinforcement Learning) is a class of algorithms that models the entire probability distribution of possible future returns an agent might receive from a given state-action pair. It provides more information than just an expected return.
Mechanism
In crypto trading, a distributional RL agent learns a value distribution, allowing it to quantify risk and uncertainty associated with different trading actions. This provides a nuanced understanding of potential outcomes, including tail risks, which is vital for institutional options and smart trading.
Methodology
Implementation involves algorithms like C51 or QR-DQN, which output a categorical or quantile distribution of returns. This method enables the construction of risk-aware trading policies, extending beyond simple expectation maximization to incorporate specific risk preferences into decision-making frameworks.
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