Using the Reinforcement Learning GitHub Package

Source: Dev.to

Introduction

In machine learning, reinforcement learning (RL) is a 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.

Categories of Machine Learning Algorithms

Reinforcement Learning: A Real‑Life Analogy

Typical Reinforcement Learning Process

Divide and Rule: Breaking Down Reinforcement Learning

A Toy Example: Grid Navigation

Why Markov Decision Processes Matter

Reinforcement Learning Implementation in R

library(MDPtoolbox)

Step 2: Define the Action Space

up <- matrix(c(
  # matrix values go here
))
# Similar matrices are defined for down, left, and right.

Step 3: Define Rewards and Penalties

Each move costs ‑1.

Step 4: Solve Using Policy Iteration

The output includes the optimal policy and value function for each state.

Step 5: Interpret the Policy

The resulting policy reveals the optimal action at each state—confirming whether the agent learned the correct path.

Using the ReinforcementLearning GitHub Package

library(devtools)

This package allows:

• Learning from Experience

  solver_rl <- ReinforcementLearning(
    # parameters defining states, actions, and rewards
  )

• Adapting to a Changing Environment

Key Takeaways

• Reinforcement learning relies on interaction rather than labeled data.
• Markov Decision Processes (MDPs) provide the formal framework for many RL problems.
• R packages such as MDPtoolbox and ReinforcementLearning enable rapid prototyping of RL algorithms.

Conclusion

Reinforcement learning offers a powerful approach for problems where an agent must learn optimal behavior through trial and error. By leveraging the available R packages, you can implement and experiment with RL models efficiently.