# Reinforcement Learning In **Reinforcement learning**, the algorithm figures out which actions to take in a situation to maximize a reward (in the form of a number) on the way to reaching a specific goal. Algorithm * Deep Q-Network (DQN) * Deep Deterministic Policy Gradient (DDPG) Applications * Industrial Robotics * Fraud Detection * Stock Trading * Autonomus Driving {% embed url="" %} ### Deep Q-Network The Deep Q-network (DQN) was introduced by Google Deepmind's group in [this paper](https://storage.googleapis.com/deepmind-media/dqn/DQNNaturePaper.pdf) in 2015. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://dailyjournal.gitbook.io/ds-ai/machine-learning/reinforcement-learning.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.