Reinforcement learning (RL)

See also: Machine learning terms

Introduction

Reinforcement learning (RL) is a subfield of machine learning that focuses on training algorithms to make decisions by interacting with an environment. The primary objective in RL is to learn an optimal behavior or strategy, often called a policy, which enables an agent to maximize its cumulative reward over time. RL algorithms are characterized by the use of trial-and-error and delayed feedback, making them particularly suitable for applications in which the optimal action is not immediately apparent and must be learned through exploration.

Components of Reinforcement Learning

Reinforcement learning consists of several key components, which together form the foundation of an RL system.

Agent

An agent is the decision-making entity that learns to interact with its environment to achieve a specific goal. The agent processes information from the environment, selects actions, and receives feedback in the form of rewards or penalties.

Environment

The environment is the external context in which the agent operates. It responds to the agent's actions and provides feedback in the form of observations, rewards, or penalties. The environment can be deterministic, where the outcomes are predetermined, or stochastic, where the outcomes are probabilistic.

State

A state represents the current situation or configuration of the environment at a given time. In reinforcement learning, an agent's goal is often to learn the optimal policy that maps states to actions.

Action

An action is a decision made by the agent to interact with the environment. The set of all possible actions that an agent can take in a given state is called the action space.

Reward

A reward is a scalar value provided by the environment as feedback to the agent, indicating the desirability of the agent's action. The objective of an RL agent is typically to maximize the cumulative reward, or the sum of rewards, over time.

Approaches to Reinforcement Learning

There are several approaches to reinforcement learning, each with its own strengths and weaknesses. Some of the most common approaches include value-based methods, policy-based methods, and model-based methods.

Value-based Methods

Value-based methods, such as Q-learning and SARSA, focus on estimating the value of actions or states. The value function represents the expected cumulative reward that the agent can obtain by following a specific policy from a given state. These methods aim to learn an optimal policy by iteratively updating the value function based on the observed rewards and state transitions.

Policy-based Methods

Policy-based methods, such as the REINFORCE algorithm and Proximal Policy Optimization (PPO), directly optimize the policy without the need for a value function. These methods adjust the policy parameters to maximize the expected cumulative reward. Policy-based methods can handle continuous action spaces and can learn stochastic policies, making them suitable for a wide range of applications.

Model-based Methods

Model-based methods involve learning a model of the environment's dynamics, which is then used to plan actions and improve the agent's policy. This approach can be more sample-efficient than model-free methods, such as value-based and policy-based methods, as it can leverage the learned model to generate additional data for training the policy.

Explain Like I'm 5 (ELI5)

Reinforcement learning is a way to teach a computer program, called an agent, how to make decisions by interacting with its surroundings, which is called the environment. The agent learns to perform better by trying different actions and receiving rewards or penalties based on how good or bad those actions are. The agent's goal is to learn the best way to make decisions so that it can get the highest rewards over time.