In machine learning, reinforcement learning (RL) is one such paradigm where problem formulation matters as much as the algorithm itself. Unlike supervised or unsupervised learning, reinforcement learning does not rely on labeled datasets. Instead, it learns through interaction, feedback, and experience. In this article, you’ll learn: What reinforcement learning is and how it differs from other ML approaches How the reinforcement learning process works conceptually How to implement reinforcement learning in R using real packages How policies, rewards, and environments shape learning outcomes

Categories of Machine Learning Algorithms Broadly, machine learning algorithms fall into three major categories: Supervised Learning Classification Regression Unsupervised Learning Clustering Dimensionality reduction Reinforcement Learning Sequential decision-making Learning through rewards and penalties Supervised and unsupervised learning have been extensively discussed and adopted across industries. Reinforcement learning, however, is fundamentally different—and far more challenging to implement correctly.

Reinforcement Learning: A Real-Life Analogy Consider a traditional classroom. A teacher introduces a concept, solves a few examples, and then asks students to practice similar problems. Students make mistakes, receive feedback, adjust their approach, and gradually improve. Reinforcement learning follows the same principle. The agent (student) interacts with an environment It takes actions Receives rewards or penalties Learns through trial and error Over time, the agent learns which actions lead to better outcomes. This learning style makes reinforcement learning particularly useful in scenarios where: Labeled data is unavailable Outcomes depend on sequences of decisions The environment changes dynamically Examples include games, robotics, navigation problems, and recommendation systems.

Typical Reinforcement Learning Process A standard reinforcement learning setup consists of: Agent – the learner or decision-maker Environment – the world the agent interacts with State (s) – the current situation Action (a) – a choice made by the agent Reward (r) – feedback from the environment Policy (π) – strategy that maps states to actions The agent’s objective is simple: maximize cumulative reward over time. Unlike supervised learning, the agent does not know the correct answer upfront—it must discover it.

Divide and Rule: Breaking Down Reinforcement Learning Reinforcement learning problems can be complex, so breaking them into manageable components is critical. To build an RL solution, you must define: Set of possible states (S) Set of actions (A) available in each state Reward and penalty structure (R) Policy (π) that guides decisions Value function (V) to evaluate long-term rewards This structure is commonly formalized using a Markov Decision Process (MDP).

A Toy Example: Grid Navigation Let’s start with a simple example—a grid navigation problem. The agent starts at a defined position The goal is to reach the exit Certain paths lead to penalties (pits or walls) Each step incurs a small penalty Reaching the goal provides a large reward The agent can move in four directions: UP DOWN LEFT RIGHT Through repeated interactions, the agent learns the optimal sequence of actions that minimizes penalties and maximizes reward.

Why Markov Decision Processes Matter Reinforcement learning typically assumes the Markov property: The next state depends only on the current state and action—not on past history. This simplifies learning and allows us to represent problems using: Transition probabilities Reward matrices Policies With this foundation in place, we can implement reinforcement learning in R.

Reinforcement Learning Implementation in R Package 1: MDPtoolbox The MDPtoolbox package provides a clean way to solve Markov decision problems using policy iteration and value iteration. Step 1: Install and Load the Package

install.packages("MDPtoolbox")

library(MDPtoolbox)

Step 2: Define the Action Space Each action (up, down, left, right) is represented as a state transition probability matrix. Each row sums to 1, ensuring valid probabilities.

Up action

up <- matrix(c( 1, 0, 0, 0, 0.7, 0.2, 0.1, 0, 0, 0.1, 0.2, 0.7, 0, 0, 0, 1 ), nrow = 4, byrow = TRUE)

(Similar matrices are defined for down, left, and right.)

Step 3: Define Rewards and Penalties Rewards <- matrix(c( -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, 10, 10, 10, 10 ), nrow = 4, byrow = TRUE)

Each move costs -1 Reaching the goal yields +10

Step 4: Solve Using Policy Iteration solver <- mdp_policy_iteration( P = Actions, R = Rewards, discount = 0.1 )

The output includes: Optimal policy Value of each state Number of iterations Execution time

Step 5: Interpret the Policy names(Actions)[solver$policy]

This reveals the optimal action at each state—confirming whether the agent learned the correct path.

Using the ReinforcementLearning GitHub Package For more exploratory experiments, the ReinforcementLearning package provides simulation-based learning. Since it’s experimental, it must be installed from GitHub:

install.packages("devtools")

library(devtools) install_github("nproellochs/ReinforcementLearning") library(ReinforcementLearning)

This package allows: Sampling experiences Learning from interaction logs Applying RL to prebuilt environments like gridworld and tic-tac-toe

Learning from Experience sequences <- sampleExperience( N = 1000, env = gridworldEnvironment, states = states, actions = actions )

solver_rl <- ReinforcementLearning( sequences, s = "State", a = "Action", r = "Reward", s_new = "NextState" )

Here, the agent learns purely from experience, reinforcing correct behavior over time.

Adapting to a Changing Environment Reinforcement learning truly shines when environments evolve. The built-in tic-tac-toe dataset demonstrates how agents learn optimal strategies from hundreds of thousands of game states—without explicit rules. data("tictactoe") model_tic_tac <- ReinforcementLearning( tictactoe, s = "State", a = "Action", r = "Reward", s_new = "NextState" )

Key Takeaways Reinforcement learning mimics human learning through trial and error Problem formulation is more important than algorithm choice R provides multiple ways to experiment with RL concepts RL is ideal for sequential, dynamic, and interactive problems

Conclusion Reinforcement learning is still evolving, but its ability to model human-like decision-making makes it one of the most exciting areas in machine learning. From navigation and games to automation and adaptive systems, reinforcement learning enables machines to learn not just from data—but from experience. As AI consulting and intelligent automation mature, reinforcement learning will increasingly play a critical role in systems that must adapt, optimize, and learn continuously. Keep experimenting. Keep refining. And most importantly—let the agent learn. At Perceptive Analytics, our mission is “to enable businesses to unlock value in data.” For over 20 years, we’ve partnered with more than 100 clients—from Fortune 500 companies to mid-sized firms—to solve complex data analytics challenges. Our services include helping organizations Hire Power BI Consultants and delivering end-to-end AI consulting services, turning data into strategic insight. We would love to talk to you. Do reach out to us.