what is practical-reinforcement-learning---coursera-advanced-machine-learning fr?

anil-matcha/practical-reinforcement-learning---coursera-advanced-machine-learning — explained in plain English

Analysis updated 2026-07-21 · repo last pushed 2019-04-02

Jupyter NotebookAudience · learnerComplexity · 2/5DormantSetup · easy

vibe map

mindmap
  root((repo))
    What it is
      Coursera assignment solutions
      Completed Jupyter notebooks
      Reinforcement learning reference
    What it covers
      Trial and error learning
      Agent environment interaction
      Foundational RL algorithms
    How to use it
      Open notebooks directly
      Run code and experiment
      Compare with your solutions
    Who it is for
      Coursera students
      Hands on learners
      RL beginners
    Format
      Mix of text and code
      No separate setup needed
      Browse files for topics

mindmap root((repo)) What it is Coursera assignment solutions Completed Jupyter notebooks Reinforcement learning reference What it covers Trial and error learning Agent environment interaction Foundational RL algorithms How to use it Open notebooks directly Run code and experiment Compare with your solutions Who it is for Coursera students Hands on learners RL beginners Format Mix of text and code No separate setup needed Browse files for topics

what do people make with this?

VIBE 1

Compare your Coursera assignment solutions against working reference notebooks.

VIBE 2

Get unstuck on a reinforcement learning assignment by reviewing completed code.

VIBE 3

Learn reinforcement learning hands-on by running notebooks that train agents to solve simple tasks.

VIBE 4

Browse the notebooks to see which foundational RL topics and algorithms are covered.

what's the stack?

Jupyter NotebookPython

how it stacks up fr

	anil-matcha/practical-reinforcement-learning---coursera-advanced-machine-learning	akshit-python-programmer/text-detection-using-neural-network	allentdan/fpn_tensorflow
Stars	—	0	—
Language	Jupyter Notebook	Jupyter Notebook	Jupyter Notebook
Last pushed	2019-04-02	—	2019-03-26
Maintenance	Dormant	—	Dormant
Setup difficulty	easy	easy	hard
Complexity	2/5	2/5	4/5
Audience	learner	vibe coder	researcher

Figures from each repo's GitHub metadata at analysis time.

how do i run it?

Difficulty · easy time til it works · 5min

Just open the notebooks in Jupyter and run them, no separate project setup is needed.

No license information is provided, so usage rights are unclear.

in plain english

This repository is a collection of completed assignments from the Practical Reinforcement Learning course, which is part of the Advanced Machine Learning specialization on Coursera. If you are working through that course and want to compare your solutions or need help getting unstuck, these notebooks give you a working reference. Reinforcement learning is a branch of machine learning where a computer agent learns to make good decisions through trial and error. The agent interacts with an environment, receives rewards or penalties, and gradually figures out strategies that maximize its long-term success. The assignments here walk through building such agents step by step, covering foundational algorithms and techniques used in the field. The content is aimed at people studying reinforcement learning who want practical, hands-on experience rather than pure theory. A typical scenario might involve training an agent to balance a pole, navigate a grid, or play a simple game. Instead of reading about how these algorithms work, you open a notebook, run the code, and see the agent improve over time. The repository consists entirely of Jupyter notebooks, which mix explanatory text with runnable code. That format makes it easy to read along and experiment without setting up a separate project from scratch. The README itself does not go into detail about what each notebook covers, so you would need to browse the files directly to see which topics are included. If you are enrolled in the Coursera specialization, this collection can serve as a helpful companion as you work through the same material.

prompts (copy fr)

prompt 1

I am stuck on a reinforcement learning assignment from the Coursera Practical RL course where an agent learns to balance a pole. Walk me through how to approach this step by step using trial and error learning.

prompt 2

Help me understand how a reinforcement learning agent interacts with an environment to maximize rewards, using a simple grid navigation example with Python code I can run in a Jupyter notebook.

prompt 3

I want to learn reinforcement learning by experimenting with completed assignment notebooks. Show me how to train a simple agent that plays a basic game and improves over time, and explain each part of the code.

prompt 4

Compare my reinforcement learning agent code with a reference solution. What are common mistakes beginners make when building agents that learn through trial and error, and how do I fix them?

Frequently asked questions