Reinforcement Learning definition: Uses and other key concepts

Reinforcement Learning (RL) is a type of Machine Learning where an Artificial Intelligence (AI) agent learns to make decisions by performing actions and receiving feedback. Through these actions, an autonomous agent learns how to perform a task by trial and error in the absence of any guidance from a human user.

RL works with the help of an agent which learns to make decisions by performing several actions for which it receives feedback in the form of rewards or penalties.

Now, the agent's goal is to maximise ‌cumulative reward over time. This is achieved through a process of trial and error, where the agent continuously updates its strategy based on the outcomes of its actions.

Over time, the agent learns to choose actions that increase the likelihood of achieving the best results in a given situation.

RL is used effectively in developing autonomous vehicles.

Autonomous vehicles interact with a complex environment that includes varying road conditions, obstacles‌ and traffic signals. This trains the car to make decisions based on real-time data, speed, distance from other objects‌ and traffic rules.

Further, each decision leads to a consequence, and the system receives feedback in the form of rewards or penalties. For example, maintaining a safe distance from the car ahead might result in a reward, while a near-miss might result in a penalty.

Over time, the system learns the right actions to take in different situations to improve safety and efficiency on the road.

Reinforcement Learning has a wide range of applications across various industries and fields. Here are some of the most notable uses…

Gaming

RL is used in video games to develop AI that can challenge human players in the games. For example, AlphaGo, developed by DeepMind, uses RL to equal and often outperform humans.

Robotics

RL helps robots learn complex tasks like walking, picking up things or navigating through environments, all through trial and error.

Autonomous vehicles

RL is crucial in developing autonomous vehicles that make real-time decisions based on dynamic road conditions, such as changing lanes, avoiding obstacles‌ or adjusting speed.

Finance

RL develops trading strategies that can adapt to changing market conditions without human intervention.

Supply chain and logistics

RL algorithms can optimise inventory management, reduce costs‌ and improve delivery times by Learning the best strategies for warehouse management, route planning‌ and resource allocation.

Here are 5 key concepts of RL that are fundamental to understanding how it works:

Agent

In RL, an agent is the decision-maker or learner. It interacts with the environment by performing actions and receiving feedback with an objective to learn and maximises the cumulative reward it receives over time.

Environment

The environment is the surrounding in which the agent operates. It could include variables, values, rules‌, regulations and valid actions.

Reward

A reward is positive feedback given to the agent after it takes an action in a particular state. This concept is crucial as it defines what are the desirable outcomes that the agent should aim for in the environment.

Policy

The policy is what the agent learns during the training process, and finding the right policy is the central goal of many Reinforcement Learning algorithms.

Here are the primary types of RL 👇

Model-based RL

In this model, the agent builds a model of the environment based on its interactions. This model represents how actions lead to different states and rewards by simulating future steps, which helps in making informed decisions.

Model-free RL

Model-free RL methods learn a policy directly from the experiences gained through interaction with the environment.

Actor-critic methods

In this method, the ‘actor’ learns the policy that specifies the action to take, while the ‘critic’ estimates the value function. This combination helps in stabilising the updates during training.

Offline RL

Also known as ‘Batch RL’, Offline RL involves Learning from a fixed dataset of experience which is useful when interacting with the environment. Additionally, offline RL relies on the optimisation of the decision-making policy using the pre-collected data.

Multi-agent RL

In multi-agent RL, multiple agents learn simultaneously in the same environment. These agents may cooperate, compete or do both while being introduced to complexity. Also, each type of RL has a choice of method which depends on the specific requirements of the application, such as the availability of data and the environment.