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.
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