About this document

Deep Reinforcement Learning Overview by Jeffy Shiny is a document available to read on EtoBox.

Deep reinforcement learning (DRL) combines reinforcement learning with deep neural networks, allowing agents to learn optimal actions in complex environments to maximize rewards. Key components include the environment, agent, reward signal, and learning algorithm, with applications in diverse fields such as robotics, autonomous driving, finance, and healthcare. Despite challenges like data requirements and safety concerns, DRL shows significant potential for real-world applications and continues to be an ac

Author

Jeffy Shiny

Language

EN