Book · Machine Learning with PyTorch and Scikit-Learn

Reinforcement Learning in PyTorch

A reinforcement-learning milestone that moves from custom GridWorld and tabular Q-learning to Gym CartPole and Deep Q-Learning.

GridWorldQ-learningGym environmentsCartPoleDeep Q-Learning

GitHub

What this project is about

This book milestone starts with custom GridWorld and tabular Q-learning, then moves into Gym CartPole and neural Deep Q-Learning.

Implementation focus

It connected environment design, action selection, reward signals, Q-value updates, and function approximation in PyTorch.

Key results and artifacts

Custom environment with states/actions/rewards
CartPole action space: Discrete(2)
DQN trained for 200 episodes

What I learned

This connected the earlier Coursera RL theory with hands-on PyTorch implementation and environment interaction.

Milestone deep dive

What this project proves in my learning path.

A reinforcement-learning milestone that moves from custom GridWorld and tabular Q-learning to Gym CartPole and Deep Q-Learning. The milestone is included because it shows a complete learning unit: a problem, a data representation, an implementation path, measurable outputs, and a lesson that later connects to larger AI systems.

Input layerstate, action, reward, and next-state transitions

Model layervalue estimation or policy learning loop

Evaluation layertests, metrics, plots, or qualitative output

Artifact layerlearned action preferences and reward curves

Problem framing

What I was trying to solve

This page is not just a lab description. It explains how I transformed the assignment idea into an engineering story: what the model receives, what it optimizes, what output it should produce, and how the result can be inspected.

Implementation

What I coded or assembled

The implementation focus was the mechanism behind the result: functions, model blocks, training logic, preprocessing, evaluation, and the small technical details that make the notebook work rather than only look correct.

Concepts

Core concepts

GridWorldQ-learningGym environmentsCartPoleDeep Q-Learning

These tags show the technical ideas this milestone added to my toolkit.

Learning result

What changed after it

This connected the earlier Coursera RL theory with hands-on PyTorch implementation and environment interaction.

Important outputs and evidence

Custom environment with states/actions/rewards
CartPole action space: Discrete(2)
DQN trained for 200 episodes

How I would explain it in an interview

I would describe this milestone by starting from the data representation, then explain the model or algorithm, then show what was measured. The important part is not only the final number; it is the ability to reason about why the method works, what assumptions it makes, and what limitations should be improved next.