We’ve heard of the terms AI (Artificial Intelligence) and Machine Learning, but what about Reinforcement Learning and why is this so important in the field of AI?
Reinforcement learning (RL) is a breakthrough in machine learning because it allows models to learn from their own actions and experiences in an environment, rather than relying on labeled data. RL is based on the idea of an agent learning to take actions in an environment to maximize a reward signal. This allows the agent to learn from its own mistakes and improve its decision-making over time, making it well-suited for tasks such as playing games, controlling robots, and optimizing control systems.
RL has been used to achieve superhuman performance in a variety of games, such as Go, chess, and poker. This has been done using deep reinforcement learning (DRL), which combines RL with deep neural networks to allow for more advanced decision-making. DRL has also been used in a variety of real-world applications, such as robotics, self-driving cars, and energy management.
Overall, RL is a breakthrough in machine learning because it allows models to learn from their own experiences and improve their decision-making, making it well-suited for a wide range of tasks, and with the integration of deep neural network, the performance has been even more advanced, making it practical to apply in real-world scenarios.