What is Reinforcement Learning?

Reinforcement Learning (RL) is a branch of machine learning in which an agent learns to make decisions by interacting with its environment. In RL, the agent’s goal is to learn a policy (a strategy) for choosing actions that maximize cumulative rewards over time.

Reinforcement Learning (RL) is a branch of machine learning in which an agent learns to make decisions by interacting with an environment. In RL, the agent's goal is to learn a policy (a strategy) for choosing actions that maximize cumulative reward over time.

Unlike supervised learning, which requires labeled examples, RL relies on trial-and-error feedback: actions that produce positive outcomes (rewards) are reinforced, while those yielding negative results (punishments) are avoided.

RL is essentially "a computational approach to understanding and automating goal-directed learning and decision-making" where the agent learns from direct interaction with its environment, without requiring external supervision or a complete model of the world.
— Sutton and Barto, Reinforcement Learning Researchers

In practice, this means the agent continually explores the state‐action space, observing the results of its actions, and adjusting its strategy to improve future rewards.

Key Concepts and Components

Reinforcement learning involves several core elements. In broad terms, an agent (the learner or decision-making entity) interacts with an environment (the external system or problem space) by taking actions at discrete time steps.

At each step the agent observes the current state of the environment, executes an action, and then receives a reward (a numerical feedback signal) from the environment. Over many such interactions, the agent seeks to maximize its total (cumulative) reward.

Agent

The autonomous learner (e.g. an AI program or robot) that makes decisions.

Environment

The world or problem domain with which the agent interacts. The environment provides the current state to the agent and computes the reward based on the agent's action.

Action

A decision or move taken by the agent to influence the environment. Different actions may lead to different states and rewards.

State

A representation of the environment at a given time (for example, the position of pieces on a game board or sensor readings in a robot). The agent uses the state to decide its next action.

Reward

A scalar feedback signal (positive, negative, or zero) given by the environment after each action. It quantifies the immediate benefit (or cost) of the action. The agent's goal is to maximize the expected cumulative reward over time.

Policy

The agent's strategy for choosing actions, typically a mapping from states to actions. Through learning, the agent aims to find an optimal or near-optimal policy.

Value Function

An estimate of the expected future reward (cumulative reward) that the agent will obtain from a given state (or state-action pair). The value function helps the agent evaluate long-term consequences of actions.

Model (Optional)

In model-based RL, the agent builds an internal model of the environment's dynamics (how states transition given actions) and uses it to plan. In model-free RL, no such model is built; the agent learns purely from trial-and-error experience.

Key Concepts and Components Reinforcement Learning

Key concepts and components of reinforcement learning framework

How Reinforcement Learning Works

RL is often formalized as a Markov decision process (MDP). At each discrete time step, the agent observes a state St and selects an action At. The environment then transitions to a new state St+1 and emits a reward Rt+1 based on the action taken.

Over many episodes, the agent accumulates experience in the form of state–action–reward sequences. By analyzing which actions led to higher rewards, the agent gradually improves its policy.

Exploration vs. Exploitation: RL problems involve a crucial trade-off between exploration and exploitation. The agent must exploit the best-known actions to gain reward, but also explore new actions that might lead to even better outcomes.

For example, a reinforcement learning agent controlling a robot may usually take a proven safe route (exploitation) but sometimes try a new path (exploration) to potentially discover a faster route. Balancing this trade-off is essential for finding the optimal policy.

RL "mimics the trial-and-error learning process that humans use". A child might learn that cleaning up earns praise while throwing toys earns scolding; similarly, an RL agent learns which actions yield rewards by receiving positive feedback for good actions and negative feedback for bad ones.
— AWS Machine Learning Documentation

Over time, the agent constructs value estimates or policies that capture the best sequence of actions to achieve long-term goals.

In practice, RL algorithms accumulate rewards over episodes and aim to maximize the expected return (sum of future rewards). They learn to prefer actions that lead to high future rewards, even if those actions may not yield the highest immediate reward. This ability to plan for long-term gain (sometimes accepting short-term sacrifices) makes RL suitable for complex, sequential decision tasks.

How reinforcement learning works in practice

Types of Reinforcement Learning Algorithms

There are many algorithms to implement reinforcement learning. Broadly, they fall into two classes: model-based and model-free methods.

Model-Based RL

Planning Approach

The agent first learns or knows a model of the environment's dynamics (how states change and how rewards are given) and then plans actions by simulating outcomes.

Efficient with limited data
Can plan ahead effectively
Requires accurate environment model

Example: A robot mapping out a building to find the shortest route is using a model-based approach.

Model-Free RL

Direct Learning

The agent has no explicit model of the environment and learns solely from trial and error in the real (or simulated) environment.

No environment model needed
Works with complex environments
Requires more experience

Example: Most classic RL algorithms (like Q-learning or Temporal-Difference learning) are model-free.

Within these categories, algorithms differ in how they represent and update the policy or value function. For example, Q-learning (a value-based method) learns estimates of the "Q-values" (expected return) for state-action pairs and picks the action with the highest value.

Policy-gradient methods directly parameterize the policy and adjust its parameters via gradient ascent on expected reward. Many advanced methods (such as Actor-Critic or Trust Region Policy Optimization) combine value estimation and policy optimization.

Deep Reinforcement Learning: A major recent development where deep neural networks serve as function approximators for value functions or policies, allowing RL to handle high-dimensional inputs like images. DeepMind's success on Atari games and board games (e.g. AlphaGo in Go) come from combining deep learning with RL.

In deep RL, algorithms like Deep Q-Networks (DQN) or Deep Policy Gradients scale RL to complex real-world tasks.

Common RL algorithms include Q-learning, Monte Carlo methods, policy-gradient methods, and Temporal-Difference learning, and "Deep RL" refers to the use of deep neural networks in these methods.
— AWS Machine Learning Documentation

Types of reinforcement learning algorithms

Applications of Reinforcement Learning

Reinforcement learning is applied in many domains where sequential decision-making under uncertainty is crucial. Key applications include:

Games and Simulation

RL famously mastered games and simulators. DeepMind's AlphaGo and AlphaZero learned Go and Chess at superhuman levels using RL.

Video games (Atari, StarCraft)
Board games (Go, Chess)
Physics simulations
Robotics simulators

Robotics and Control

Autonomous robots and self-driving cars are agents in dynamic environments learning through trial and error.

Object grasping and manipulation
Autonomous navigation
Self-driving vehicles
Industrial automation

Recommendation Systems

RL can personalize content or ads based on user interactions, learning to present the most relevant items over time.

Content personalization
Ad targeting optimization
Product recommendations
User engagement optimization

Resource Optimization

RL excels in optimizing systems with long-term objectives and complex resource allocation challenges.

Data center cooling optimization
Smart grid energy storage
Cloud computing resources
Supply chain management

Finance and Trading

Financial markets are dynamic and sequential, making RL suitable for trading strategies and portfolio management.

Algorithmic trading strategies
Portfolio optimization
Risk management
Market making

Long-term Planning Advantage: These applications highlight RL's strength in long-term planning. Unlike methods that only predict immediate outcomes, RL explicitly maximizes cumulative rewards, making it well-suited for problems where actions have delayed consequences.

Applications of reinforcement learning across industries

Reinforcement Learning vs. Other Machine Learning

Reinforcement learning is one of the three major paradigms of machine learning (alongside supervised and unsupervised learning), but it is quite different in focus. Supervised learning trains on labeled input-output pairs, while unsupervised learning finds patterns in unlabeled data.

Aspect	Supervised Learning	Unsupervised Learning	Reinforcement Learning
Data Type	Labeled input-output pairs	Unlabeled data	Sequential state-action-reward tuples
Learning Goal	Predict correct outputs	Find hidden patterns	Maximize cumulative reward
Feedback Type	Direct correct answers	No feedback	Reward/punishment signals
Learning Method	Learn from examples	Discover structure	Trial-and-error exploration

In contrast, RL does not require labeled examples of correct behavior. Instead, it defines a goal via the reward signal and learns by trial and error. In RL, the "training data" (state-action-reward tuples) are sequential and interdependent, because each action affects future states.

Put simply, supervised learning tells a model what to predict; reinforcement learning teaches an agent how to act. RL learns by "positive reinforcement" (reward) rather than by being shown the correct answers.
— IBM Machine Learning Overview

This makes RL particularly powerful for tasks that involve decision-making and control. However, it also means RL can be more challenging: without labeled feedback, the agent must discover good actions on its own, often requiring much exploration of the environment.

Reinforcement learning vs other machine learning paradigms

Challenges of Reinforcement Learning

Despite its power, RL comes with practical challenges:

Sample Inefficiency

RL often requires vast amounts of experience (trials) to learn effective policies. Training in the real world can be costly or slow (for example, a robot may need millions of trials to master a task). For this reason, many RL systems are trained in simulation before deployment.

Reward Design

Defining an appropriate reward function is tricky. A poorly chosen reward can lead to unintended behaviors (the agent may "game" the reward in a way that doesn't align with the true goal). Designing rewards that capture long-term objectives without unintended shortcuts is an art in RL research.

Stability and Safety

In real-world settings (robotics, healthcare, finance), unsafe exploratory actions can be dangerous or costly. Real-world experimentation (e.g. flying a drone) may not be practical without simulation. Ensuring safety during learning and deployment is an active area of RL research.

Interpretability

Learned RL policies (especially deep RL models) can be opaque. Understanding why an agent takes certain actions is often difficult, making it hard to debug or trust the system. This lack of interpretability is noted as a deployment challenge for complex RL systems.

Ongoing Research: Each of these challenges is the subject of ongoing research. Despite the hurdles, the practical successes of RL (in games, robotics, recommender systems, etc.) demonstrate that when applied carefully, RL can achieve impressive results.

Challenges of reinforcement learning implementation

Conclusion

In summary, reinforcement learning is an autonomous learning framework in which an agent learns to achieve goals by interacting with its environment and maximizing cumulative reward. It combines ideas from optimal control, dynamic programming, and behavioral psychology, and it is the foundation of many modern AI breakthroughs.

By framing problems as sequential decision-making tasks with feedback, RL enables machines to learn complex behaviors on their own, bridging the gap between data-driven learning and goal-directed action.

Explore more related articles

External References

This article has been compiled with reference to the following external sources:

Basic Knowledge of AI

25/08/2025

Rosie Ha

87 articles

Rosie Ha is an author at Inviai, specializing in sharing knowledge and solutions about artificial intelligence. With experience in researching and applying AI across various fields such as business, content creation, and automation, Rosie Ha delivers articles that are clear, practical, and inspiring. Her mission is to help everyone effectively harness AI to boost productivity and expand creative potential.

View Profile Profile All Posts (87) Posts (87)