Reinforcement Learning with Human Feedback (RLHF) for algorithmic trading

The success of ChatGPT brings the Reinforcement Learning with Human Feedback (RLHF) technique under the spotlight. RLHF is a type of machine learning approach that combines reinforcement learning (RL) and human feedback (HF) to improve the learning process. This post will give you a comprehensive understanding for RLHF. It describes RLHF applications in algorithmic trading (algo trading) and provides executable Python code examples. In the code examples, I will present a code example that does not have RLHF, then add RLHF to the code examples. I believe this is a natural way to learn a topic. I gradually take you deeper to the components in RLHF including Epsilon-greedy policy and Q-learning update rule. This will equip algorithmic traders for RLHF. <a href="https://medium.com/@dataman-ai/reinforcement-learning-with-human-feedback-rlhf-for-algorithmic-trading-86e1c83b0e1d">Read More</a>