Reinforcement Learning is a machine learning algorithm that enables computers to make decisions in dynamic and potentially complex environments. It is a subfield of machine learning inspired by human behavior. It is an approach used by artificial intelligence (AI) agents to learn by trial and error and optimize the expected total reward for a given problem. The designer establishes the reward policy, which includes setting rules for the agent. However, no guidance is provided on how to solve the problem. Thus, the model must discover for itself the optimal method to maximize the reward. Reinforcement learning allows machines to gain creativity by leveraging its search capabilities and numerous trials.
Applications of Reinforcement Learning
Reinforcement Learning
- Powers autonomous vehicles to make decisions in complex driving scenarios, such as navigating through intersections, merging onto highways, and avoiding obstacles.
- Helps recommend products, services, or content to individual users based on their preferences and behavior.
- Helps develop personalized treatment plans for patients with chronic conditions such as diabetes or cancer. Reinforcement learning can also optimize hospital operations, such as patient scheduling, bed allocation, and resource allocation.
- Can develop robots that can learn to perform complex tasks such as grasping objects, walking, and navigating through space. It can also be used to optimize the behavior of robots in dynamic environments.
- Can be used to develop agents that can play complex games such as chess, Go, and poker at a high level. In some cases, these agents have even surpassed human experts in terms of performance.
