Introduction to Reinforcement Learning
Autonomous systems are systems that operate without human intervention. Reinforcement Learning (RL) is a popular approach for training autonomous systems. In RL, an agent learns to take actions in an environment to maximize some reward signal. Autonomous systems that use RL have a wide range of applications, such as self-driving cars, drones, and smart homes.
One example of an autonomous system that uses RL is a self-driving car. The car's RL agent learns to take actions (such as accelerating, braking, and steering) based on the car's sensors and the environment (such as traffic and road conditions) to achieve a goal (such as reaching a destination as quickly and safely as possible). The agent receives a reward signal (such as a positive reward for reaching the destination or a negative reward for colliding with an obstacle) for each action it takes, and uses these rewards to learn which actions are best in which situations.
Another example of an autonomous system that uses RL is a drone. The drone's RL agent learns to take actions (such as adjusting altitude and direction) based on the drone's sensors and the environment (such as wind and obstacles) to achieve a goal (such as delivering a package to a specific location). The agent receives a reward signal (such as a positive reward for successful delivery or a negative reward for damaging the package) for each action it takes, and uses these rewards to learn which actions are best in which situations.
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