Reinforcement learning (RL) is a type of machine learning paradigm where an agent learns to make decisions by interacting with an environment. In reinforcement learning, the agent's goal is to learn a policy, which is a mapping from states of the environment to actions, in order to maximize cumulative rewards over time.
The key components of reinforcement learning are :
* Agent : The learner or decision-maker that interacts with the environment.
* Environment : The external system with which the agent interacts, and from which the agent receives feedback in the form of rewards.
* Actions : The set of possible moves or decisions that the agent can make.
* States : The current situation or configuration of the environment.
* Rewards : The numerical feedback from the environment to the agent, indicating how favorable the outcome of an action was.
* Policy : The strategy or behavior that the agent employs to determine its actions in different states.
Reinforcement learning algorithms typically aim to find the optimal policy that maximizes the cumulative reward over time. This is achieved through a process of trial and error, where the agent learns from its experiences by trying different actions and observing the rewards obtained. RL algorithms often utilize concepts from dynamic programming, optimization, and control theory to efficiently learn good policies in complex environments.
RL has applications in a wide range of domains, including
robotics, gaming, finance, healthcare, and more..
