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Grokking Deep Reinforcement Learning
| Date | Stars |
|---|---|
| 2026-07-24 | 1030 |
| 2026-07-25 | 1030 |
| 2026-07-28 | 1030 |
| 2026-07-30 | 1030 |
| 2026-07-31 | 1033 |
| 2026-08-02 | 1034 |
| 2026-08-05 | 1036 |
| 2026-08-06 | 1036 |
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# Grokking Deep Reinforcement Learning
**Note:** At the moment, only running the code from the [docker](https://github.com/docker/docker-ce) container (below) is supported. Docker allows for creating a single environment that is more likely to work on all systems. Basically, I install and configure all packages for you, except docker itself, and you just run the code on a tested environment.
To install docker, I recommend a web search for "installing docker on \<your os here>". For running the code on a GPU, you have to additionally install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker). NVIDIA Docker allows for using a host's GPUs inside docker containers. After you have docker (and nvidia-docker if using a GPU) installed, follow the three steps below.
## Running the code
0. Clone this repo:
`git clone --depth 1 https://github.com/mimoralea/gdrl.git && cd gdrl`
1. Pull the gdrl image with:
`docker pull mimoralea/gdrl:v0.14`
2. Spin up a container:
- On Mac or Linux:
`docker run -it --rm -p 8888:8888 -v "$PWD"/notebooks/:/mnt/notebooks/ mimoralea/gdrl:v0.14`
- On Windows:
`docker run -it --rm -p 8888:8888 -v %CD%/notebooks/:/mnt/notebooks/ mimoralea/gdrl:v0.14`
- NOTE: Use `nvidia-docker` or add `--gpus all` after `--rm` to the command, if you are using a GPU.
3. Open a browser and go to the URL shown in the terminal (likely to be: http://localhost:8888). The password is: `gdrl`
## About the book
### Book's website
https://www.manning.com/books/grokking-deep-reinforcement-learning
### Table of content
1. [Introduction to deep reinforcement learning](#1-introduction-to-deep-reinforcement-learning)
2. [Mathematical foundations of reinforcement learning](#2-mathematical-foundations-of-reinforcement-learning)
3. [Balancing immediate and long-term goals](#3-balancing-immediate-and-long-term-goals)
4. [Balancing the gathering and utilization of information](#4-balancing-the-gathering-and-utilization-of-information)
5. [Evaluating agents' behaviors](#5-evaluating-agents-behaviors)
6. [Improving agents' behaviors](#6-improving-agents-behaviors)
7. [Achieving goals more effectively and efficiently](#7-achieving-goals-more-effectively-and-efficiently)
8. [Introduction to value-based deep reinforcement learning](#8-introduction-to-value-based-deep-reinforcement-learning)
9. [More stable value-based methods](#9-more-stable-value-based-methods)
10. [Sample-efficient value-based methods](#10-sample-efficient-value-based-methods)
11. [Policy-gradient and actor-critic methods](#11-policy-gradient-and-actor-critic-methods)
12. [Advanced actor-critic methods](#12-advanced-actor-critic-methods)
13. [Towards artificial general intelligence](#13-towards-artificial-general-intelligence)
### Detailed table of content
#### 1. Introduction to deep reinforcement learning
- \([Livebook](https://livebook.manning.com/book/grokking-deep-reinforcement-learning/chapter-1)\)
- \(No Notebook\)
#### 2. Mathematical foundations of reinforcement learning
- \([Livebook](https://livebook.manning.com/book/grokking-deep-reinforcement-learning/chapter-2)\)
- \([Notebook](/notebooks/chapter_02/chapter-02.ipynb)\)
- Implementations of several MDPs:
- Bandit Walk
- Bandit Slippery Walk
- Slippery Walk Three
- Random Walk
- Russell and Norvig's Gridworld from AIMA
- FrozenLake
- FrozenLake8x8
#### 3. Balancing immediate and long-term goals
- \([Livebook](https://livebook.manning.com/book/grokking-deep-reinforcement-learning/chapter-3)\)
- \([Notebook](/notebooks/chapter_03/chapter-03.ipynb)\)
- Implementations of methods for finding optimal policies:
- Policy Evaluation
- Policy Improvement
- Policy Iteration
- Value Iteration
#### 4. Balancing the gathering and utilization of information
- \([Livebook](https://livebook.manning.com/book/grokking-deep-reinforcement-learning/chapter-4)\)
- \([Notebook](/notebooks/chapter_04/cExcerpt of 8,036 characters
Read on GitHub
Miguel Morales · Colombia
164
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:7ef487c2203c928e, topic:reinforcement-learning, topic:deep-reinforcement-learning, desc:reinforcement learning
matched fp:7ef487c2203c928e, topic:deep-learning, topic:pytorch