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DQN Zoo is a collection of reference implementations of reinforcement learning agents developed at DeepMind based on the Deep Q-Network (DQN) agent.
| Date | Stars |
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| 2026-07-31 | 509 |
| 2026-08-03 | 509 |
| 2026-08-04 | 509 |
| 2026-08-06 | 509 |
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# DQN Zoo
*DQN Zoo* is a collection of reference implementations of reinforcement learning
agents developed at DeepMind based on the
[Deep Q-Network (DQN)](http://www.nature.com/articles/nature14236) agent.
It aims to be research-friendly, self-contained and readable. Each agent is
implemented using [JAX](http://github.com/google/jax),
[Haiku](http://github.com/deepmind/haiku) and
[RLax](http://github.com/deepmind/rlax), and is a best-effort replication of the
corresponding paper implementation. Each agent reproduces results on the
standard set of 57 Atari games, on average.
<!-- mdformat off(for readability) -->
| Directory | Paper |
| ------------- | -------------------------------------------------------------------------------------------------------- |
| `dqn` | [Human Level Control Through Deep Reinforcement Learning](http\://www.nature.com/articles/nature14236) |
| `double_q` | [Deep Reinforcement Learning with Double Q-learning](http\://arxiv.org/abs/1509.06461) |
| `prioritized` | [Prioritized Experience Replay](http\://arxiv.org/abs/1511.05952) |
| `c51` | [A Distributional Perspective on Reinforcement Learning](http\://arxiv.org/abs/1707.06887) |
| `qrdqn` | [Distributional Reinforcement Learning with Quantile Regression](http\://arxiv.org/abs/1710.10044) |
| `rainbow` | [Rainbow: Combining Improvements in Deep Reinforcement Learning](http\://arxiv.org/abs/1710.02298) |
| `iqn` | [Implicit Quantile Networks for Distributional Reinforcement Learning](http\://arxiv.org/abs/1806.06923) |
<!-- mdformat on -->
Plot of median human-normalized score over all 57 Atari games for each agent:
## Quick start
NOTE: Only Python 3.9 and above and Linux is supported.
Follow these steps to quickly clone the DQN Zoo repository, install all required
dependencies and start running DQN. Prerequisites for these steps are a NVIDIA
GPU with recent CUDA drivers.
<!-- mdlint off() -->
1. Install [Docker](http://docs.docker.com/) version 19.03 or later (for the
`--gpus` flag).
1. Install [NVIDIA Container Toolkit](http://github.com/NVIDIA/nvidia-docker).
1. Enable
[sudoless docker](http://docs.docker.com/engine/install/linux-postinstall/#manage-docker-as-a-non-root-user).
1. Verify the previous steps were successful e.g. by running: \
`docker run --gpus all --rm nvidia/cuda:11.1.1-base nvidia-smi`
1. Download the script [`run.sh`](run.sh). This automatically downloads the
Atari ROMs from http://www.atarimania.com. The ROMs are available here for
free but make sure the respective license covers your particular use case.
Running this script will:
```
1. Clone the DQN Zoo repository.
1. Build a Docker image with all necessary dependencies and run unit tests.
1. Start a short run of DQN on Pong in a GPU-accelerated container.
```
<!-- mdlint on -->
NOTE: `run.sh`, `Dockerfile` and `docker_requirements.txt` together provide a
self-contained example of the dependencies and commands needed to run an agent
in DQN Zoo. Using Docker is not a requirement and if `Dockerfile` is not used
then the list of dependencies to install may have to be adapted depending on
your environment. Also it is not a hard requirement to run on the GPU. Agents
can be run on the CPU by specifying the flag `--jax_platform_name=cpu`.
## Goals
* Serve as a collection of reference implementations of DQN-based agents
developed at DeepMind.
* Reproduce results reported in papers, on average.
* Implement agents purely in Python, using JAX, Haiku and RLax.
* Have minimal dependencies.
* Be easy to read.
* Be easy to modify and customize after forking.
## Non-goals
* Be a library or framework (these agents are intended to be forked for
research).
* Be fExcerpt of 12,067 characters
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44
Georg Ostrovski · DeepMind · United Kingdom
9
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:d3f573a0ee8eee41, desc:reinforcement learning