The tech and crypto markets are unpredictable. This is a problem for standard models, like the Markowitz model, which rely on assumptions that don't always fit.

        Many experts are looking for new methods that can handle this. Deep Reinforcement Learning (DRL) is a promising approach. It uses AI to make decisions based on rewards. These rewards are calculated by a simulator. The tricky part? DRL algorithms use complex models that aren't easy to understand.

        The issue is that investors need to understand the reasons behind decisions, and DRL doesn't make that easy. The agent's actions are driven by parameters that aren't clear. This makes it hard to follow a specific policy.

        Not to worry, though. Researchers have come up with a new approach called Explainable DRL (XDRL). This method combines Proximal Policy Optimization (PPO) with tools like SHAP and LIME. These tools help make the AI's decisions easier to understand.

        It is a way to explain the actions of the agent in real time. This means investors can see if the AI's suggestions are reasonable or risky.

        This approach has been tested and it works. It can pinpoint the key factors that influence investment decisions. This makes the AI's actions understandable in real time, making investment more transparent.

        Investors want to understand why an AI makes certain decisions. XDRL offers a way to do this. It's all about making smart investment strategies clearer and more reliable.

Unlocking the Mystery of Smart Investment Strategies

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