Reinforcement learning (RL)

Reinforcement learning (RL) is a type of machine learning where an agent learns to take actions in an environment to maximize a cumulative reward. It is based on the concept of trial and error, where the agent learns from its experiences and tries to maximize the reward it receives. The agent interacts with the environment by taking actions, and the environment responds with a reward and a new state. The goal of the agent is to learn a policy, a function that maps states to actions, that maximizes the cumulative reward over time.

Markov decision process (MDP)

The most common formulation of RL is the Markov decision process (MDP). An MDP is a mathematical framework that models the interaction between an agent and an environment. It consists of a set of states, a set of actions, a reward function, and a transition function.

• The reward function defines the reward the agent receives for taking an action in a certain state.
• The transition function defines the probability of moving from one state to another when an action is taken.

Together, these two functions capture the dynamics of the environment. Given an MDP, the goal of RL is to learn a policy that maximizes the expected cumulative reward, also known as the return.

RL is a powerful technique that has been successfully applied to a wide range of problems, including game playing, robotics, and autonomous systems. For example, in game playing, an RL agent can learn to play a game by trial and error, starting from random moves and gradually improving its performance. In robotics, an RL agent can learn to control a robot by experimenting with different actions and observing the resulting movements. In autonomous systems, an RL agent can learn to make decisions based on sensor data and feedback from the environment.

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