Notes

Q Learning algorithm is: The figure below shows how a Q-learning agent trains on Q-Value function critic in order to estimate the optimal policy value. It follows epsilon greedy policy based on the value that is determined by the critic. The Internal Flow of a Q-Learning Agent Unlike basic algorithms, a Q-Learning agent splits its ... Read More

The Two Paths: Model-Free vs. Model-Based In Reinforcement Learning, the term “Model” is used to describe the rules of the environment. Specifically, a model consists of the Transition Dynamics (where an action takes you) and the Reward Function (what score that action earns). The biggest divide in algorithm design is whether the agent attempts to ... Read More

The Agent-Environment Interaction In Reinforcement Learning, the two core components are the Agent and the Environment. The environment serves as the simulated or physical world where the agent operates. 🤖 Agent Action (a_t) Observation & Reward 🌍 Environment In a continuous loop, the agent receives an observation detailing the current condition of this world. Based ... Read More

What is Deep Reinforcement Learning? Demystifying Deep Reinforcement Learning How AI learns to make decisions through trial and error. Let’s break down deep reinforcement learning (DRL). It is a type of artificial intelligence where a computer program learns to make a sequence of decisions through trial and error, guided by a system of rewards. The ... Read More

If you want to teach an Artificial Intelligence to play a video game, control a robot, or optimize a trading strategy, you need an environment for it to practice in. Enter Gymnasium (formerly OpenAI Gym), the standard API for single-agent Reinforcement Learning (RL). 1. The Agent-Environment Loop Reinforcement learning is fundamentally about trial and error. We don’t ... Read More