Policy Proximal Optimization ― A New Frontier in Reinforcement Learning
<p>Welcome to the world of reinforcement learning. One of the key algorithms used in this field of artificial intelligence is Proximal Policy Optimization (PPO). PPO is an algorithm used in reinforcement learning to train agents, but before we dive into Proximal Policy Optimization, let’s first understand a basic term used throughout this article: <strong><em>agent</em></strong>. An agent is an autonomous entity that interacts with its environment using actuators and sensors with an aim of achieving a certain goal. Examples of agents can be an automatic vacuum cleaner, a robotic arm, or a self-driving car. <strong>Reinforcement learning</strong> is a field in AI that can be used to train agents to learn how to perform certain tasks efficiently through the structure of penalties and rewards. So, at the heart of a RL program is an algorithm that rewards the agent when it performs well by awarding it certain points and penalizes it when it strays away from the intended goal. </p>
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