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Reinforcement Learning Fundamentals

Reinforcement Learning Fundamentals

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Jun 24, 2026

Reinforcement Learning (RL) is a paradigm of machine learning where an autonomous Agent learns to make sequences of decisions through trial-and-error interactions with an Environment . Unlike supervised learning, which relies on a pre-existing dataset of "correct" labels, an RL agent learns by receiving Reward signals, aiming to maximize its total cumulative reward over time .

The core objective is for the agent to develop an optimal Policy that dictates which action to take in any given State . This framework is mathematically formalized as a Markov Decision Process (MDP) .

Footnotes

  1. An Introduction to Reinforcement Learning - Overview of core RL concepts and methodologies. ↩

  2. Spinning Up: Key Concepts in RL - Introduction to the agent, environment, and reward signals. ↩

  3. Reinforcement Learning Basics - Definition of policy and environment interactions. ↩

  4. GeeksforGeeks: What is Reinforcement Learning? - Explanation of Markov Decision Processes in RL. ↩

Reinforcement Learning: Crash Course AI

RL vs. Other Learning Paradigms

While supervised learning focuses on mapping input to a known label, reinforcement learning focuses on sequential decision-making. The agent's actions influence future observations, making the learning process dynamic and temporal.

The Reinforcement Learning Loop

  1. 1
    Step 1

    The agent perceives the current state StS_t of the environment.

  2. 2
    Step 2

    Based on its policy π\pi, the agent chooses an action AtA_t to execute.

  3. 3
    Step 3

    The environment transitions to a new state St+1S_{t+1} and provides a reward Rt+1R_{t+1}.

  4. 4
    Step 4

    The agent updates its policy or value function to improve future decision-making based on the received reward.

Core Concepts of RL

Comparison of Learning Approaches

Data-driven strategy comparison

The Curse of Dimensionality

As the number of possible states and actions grows, the complexity of finding an optimal policy increases exponentially. Deep Reinforcement Learning addresses this by using neural networks to approximate value functions and policies in high-dimensional spaces.

Types of Reinforcement Learning

Model-Free

Method 1

The agent learns the policy or value function directly from interactions without attempting to model the environment dynamics."

Model-Based

Method 2

The agent learns a model of how the environment works (e.g., transition probabilities) and uses this model to plan future actions."

Knowledge Check

Question 1 of 3
Q1Single choice

What is the primary goal of a Reinforcement Learning agent?

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