1.3 Where is Reinforcement Learning Applied?
Recommendation Systems
Reinforcement Learning powers modern recommendation systems by dynamically adapting to user preferences, optimizing content suggestions in platforms like Netflix, YouTube, and Spotify using techniques such as Multi-Armed Bandits (MAB).
Trained using MAB (Lecture 2)
Link to Online Article
Games
Reinforcement Learning has revolutionized gaming by enabling AI to master complex environments, from Atari classics to advanced strategy games, using deep learning techniques like Deep Q-Networks (DQN) to achieve superhuman performance.
Trained using DQN (Lecture 9)
Link to Research Paper
Robotics
In robotics, Reinforcement Learning enables autonomous agents to learn complex motor skills, such as dexterous manipulation and locomotion, through continuous interaction and training with Proximal Policy Optimization (PPO).
Trained using PPO (Lecture 11)
Link to Blog
Autonomous Vehicles
Self-driving cars rely on Reinforcement Learning to navigate complex environments, optimize decision-making, and improve safety, often incorporating PPO and deep learning to refine real-time control strategies.
Partly trained using PPO (Lecture 11)
Natural Language Processing
Reinforcement Learning from Human Feedback (RLHF) enhances AI language models like ChatGPT, allowing them to refine responses based on user interactions and align better with human preferences.
Trained using PPO (Lecture 11)
Link to Research Paper
Finance
In financial markets, RL is applied to portfolio optimization, algorithmic trading, and risk management, leveraging techniques like PPO to make data-driven investment decisions.
Trained using PPO (Lecture 11)
Link to Research Paper