A common machine learning technique called reinforcement learning (RL) teaches an agent how to choose actions that will maximize a reward signal. By getting rewarded for activities that produce desirable results, the agent learns from its environment. The reward signal, however, may not be clear in many real-world situations or may be challenging to get. In these circumstances, human feedback can provide the agent the direction it needs to learn effectively. Reinforcement Learning with Human Feedback is what this is (RLHF). In this article, we'll look at how to use Python to implement a reinforcement learning algorithm with human feedback. We'll simulate a learning challenge using the OpenAI Gym environment, and we'll construct the reinforcement learning method using the Tensorforce library . Introduction to Reinforcement Learning (RL) The goal of Reinforcement Learning (RL), a particular approach to machine learning, is to teach an agent how to make decisions in the rea