Building a Simple Reinforcement Learning Agent

Constructing a basic reinforcement learning (RL) agent entails developing a system that interacts with its surroundings to maximise cumulative rewards in order to learn how to make decisions. The first step in the process is to define the environment, which consists of states that depict the current circumstances, actions that represent the options available to the agent, and rewards that are feedback from the environment based on the activities performed. In order to learn the optimal course of action in each state through trial and error, the agent is usually constructed using algorithms like Q-learning or Deep Q-Networks (DQN). The agent gradually improves its decision-making policy by experimenting with various actions to gain experience and updating its understanding of the environment with a reward signal. To guarantee effective learning, training necessitates careful adjustment of factors such as exploration-exploitation balance and learning rate. The ability of the agent to maximise rewards over time in a variety of settings is used to measure performance. Simple RL agents offer a fundamental understanding of how agents can learn from their surroundings and optimise their behaviour, and they can be used in a wide range of applications, such as robotics, automated decision-making, and gaming.