Policy Gradients refer to a class of reinforcement learning algorithms that directly learn a parameterized policy function, which maps states to actions. They optimize the policy’s expected return through gradient ascent.
Mechanism
In smart crypto trading, a policy gradient agent directly learns optimal trading actions, such as buying, selling, holding, or specifying order quantities, based on observed market states. It adjusts its parameters to maximize long-term portfolio value or minimize risk.
Methodology
Implementation involves algorithms like REINFORCE or Actor-Critic methods, where the policy’s parameters are updated iteratively using gradients derived from sampled returns. This approach enables the system to discover complex trading strategies that adapt to dynamic market conditions without explicit value function estimation.
We use cookies to personalize content and marketing, and to analyze our traffic. This helps us maintain the quality of our free resources. manage your preferences below.
Detailed Cookie Preferences
This helps support our free resources through personalized marketing efforts and promotions.
Analytics cookies help us understand how visitors interact with our website, improving user experience and website performance.
Personalization cookies enable us to customize the content and features of our site based on your interactions, offering a more tailored experience.
