How does an agent need to become autonomous ?

In Deep Reinforcement Learning (DRL), an agent is a computational entity that interacts with an environment to learn how to perform a task. The agent observes the state of the environment, selects an action based on a policy, and receives feedback in the form of a reward signal. The agent’s goal is to maximize its cumulative reward over time, by iteratively adjusting its policy based on the feedback received from the environment. The policy is a set of rules that determines the agent’s behavior in a given state, such as which action to take. DRL agents typically use deep neural networks to learn from unstructured data such as images, audio, and text. The neural network takes the current state of the environment as input and produces a probability distribution over possible actions. The agent then selects an action based on this distribution, using techniques such as exploration and exploitation to balance the desire to maximize reward with the need to explore new actions. As the agent interacts with the environment, it receives feedback in the form of a reward signal, which is used to update the neural network weights and improve the agent’s performance over time. This iterative process of observing, selecting actions, receiving feedback, and updating the policy continues until the agent reaches a satisfactory level of performance on the task at hand.