Deep Reinforcement Learning

What is an Agent? The three core characteristics of an autonomous system. 1 Autonomous Entity It operates independently as a self-directed entity, without requiring constant human intervention or manual control. 2 Observes Environment It continuously monitors its surroundings, gathering real-time state data and information about the world around it. 3 Chooses How to Act Based ... Read More

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

Resource: Create Agent – Click New Hidden Units keep to 20.

“Reinforcement Learning Toolbox,” Mathworks.com, 2026. https://au.mathworks.com/products/reinforcement-learning.html?requestedDomain= (accessed Feb 23, 2026). Implementing Deep Reinforcement Learning Toolbox Defining Observation and Action Spaces The first stage of development involves establishing the operational boundaries. Data specifications must be defined for both observations (system states) and actions (available commands). rlNumericSpec([5 1]); % Define continuous state space rlFiniteSetSpec(1:4); % Define discrete action ... Read More