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https://github.com/google-deepmind/dqn_zoo

DQN Zoo is a collection of reference implementations of reinforcement learning agents developed at DeepMind based on the Deep Q-Network (DQN) agent.
https://github.com/google-deepmind/dqn_zoo

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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.

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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.



| 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) |



Plot of median human-normalized score over all 57 Atari games for each agent:



![Plot summary](plot_atari_summary.svg)



## 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.



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.

```



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 flexible, general and support multiple use cases (at odds with

    understandability).

*   Support many environments (users can easily add new ones).

*   Include every DQN variant that exists.

*   Incorporate many cool libraries (harder to read, easy for the user to do

    this after forking, different users prefer different libraries, less

    self-contained).

*   Optimize speed and efficiency at the cost of readability or matching

    algorithmic details in the papers (no C++, keep to a single stream of

    experience).



## Code structure



*   Each directory contains a published DQN variant configured to run on Atari.

*   `agent.py` in each agent directory contains an agent class that includes

    `reset()`, `step()`, `get_state()`, `set_state()` methods.

*   `parts.py` contains functions and classes used by many of the agents

    including classes for accumulating statistics and the main training and

    evaluation loop `run_loop()`.

*   `replay.py` contains functions and classes relating to experience replay.

*   `networks.py` contains Haiku networks used by the agents.

*   `processors.py` contains components for standard Atari preprocessing.



## Implementation notes



Generally we went with a flatter approach for easier code comprehension.

Excessive nesting, indirection and generalization have been avoided, but not to

the extreme of having a single file per agent. This has resulted in some degree

of code duplication, but this is less of a maintenance issue as the code base is

intended to be relatively static.



Some implementation details:



*   The main training and evaluation loop `parts.run_loop()` is implemented as a

    generator to decouple it from other concerns like logging statistics and

    checkpointing.

*   We adopted the pattern of returning a new JAX PRNG key from jitted

    functions. This allows for splitting keys inside jitted functions which is

    currently more efficient than splitting outside and passing a key in.

*   Agent functions to be jitted are defined inline in the agent class

    `__init__()` instead of as decorated class methods. This emphasizes such

    functions should be free of side-effects; class methods are generally not

    pure as they often alter the class instance.

*   `parts.NullCheckpoint` is a placeholder for users to optionally plug in a

    checkpointing library appropriate for the file system they are using. This

    would allow resuming an interrupted training run.

*   The preprocessing and action repeat logic lives inside each agent. Doing

    this instead of taking the common approach of environment wrappers allows

    the run loop to see the "true" timesteps. This makes things like recording

    performance statistics and videos easier since the unmodified rewards and

    observations are readily available. It also allows us to express all

    relevant flag values in terms of environment frames, instead of a more

    confusing mix of environment frames and learning steps.



## Learning curves



Learning curve data is included in [`results.tar.gz`](results.tar.gz). The

archive contains a CSV file for each agent, with statistics logged during

training runs. These training runs span the standard set of 57 Atari games, 5

seeds each, using default agent settings. Note

[Gym](http://github.com/openai/gym) was used instead of

[Xitari](http://github.com/deepmind/xitari).



These CSV files can be theoretically equivalently generated by the following

pseudocode:



```bash

for agent in "${AGENTS[@]}"; do

  for game in "${ATARI_GAMES[@]}"; do

    for seed in {1..5}; do

      python -m "dqn_zoo.${agent}.run_atari" \

          --environment_name="${game}" \

          --seed="${seed}" \

          --results_csv_path="/tmp/dqn_zoo/${agent}/${game}/${seed}/results.csv"

    done

  done

done

```



Each agent CSV file in `results.tar.gz` is then a concatenation of all

associated `results.csv` files, with additional `environment_name` and `seed`

fields. Note the learning curve data is missing `state_value` since logging for

this quantity was added after the data was generated.



Plots show the average score at periodic evaluation phases during training. Each

episode during evaluation starts with up to 30 random no-op actions and lasts a

maximum of 30 minutes. To make the plots more readable, scores have been

smoothed using a moving average with window size 10.



Plot of average score on each individual Atari game for each agent:



![Plot individual](plot_atari_individual.svg)



## FAQ



### Q: Do these agents replicate results from their respective papers?



We aim to replicate the mean and median human normalized score over all 57 Atari

games and to implement the algorithm described in each paper as closely as

possible.



However there are potential sources of differences at the level of an individual

game. These include:



*   Differences between [Gym](http://github.com/openai/gym) +

    [Arcade Learning Environment (ALE)](http://github.com/mgbellemare/Arcade-Learning-Environment)

    and [Xitari](http://github.com/deepmind/xitari).

*   Changes in underlying libraries such as the exact image resizing algorithm

    used in the observation preprocessing.

*   Atari ROM version.



### Q: Is the execution of these agents deterministic?



We try to allow for it on CPU. However it is easily broken and note that

convolutions on GPU are not deterministic. To allow for determinism we:



*   Build a new environment at the start of every iteration.

*   Include in the training state:

    *   Random number generator state.

    *   Target network parameters (in addition to online network parameters).

    *   Evaluation agent.



### Q: Why is DQN-based agent X not included?



There was a bias towards implementing the variants the authors are most familiar

with. Also one or more of the following reasons may apply:



*   Did not get round to implementing X.

*   Have yet to replicate the algorithmic details and learning performance of X.

*   It is easy to create X from components in DQN Zoo.



### Q: Why not incorporate library / environment X?



X is probably very useful, but every additional library or feature is another

thing new users need to read and understand. Also everyone differs in the

auxiliary libraries they like to use. So the recommendation is to fork the agent

you want and incorporate the features you wish in the copy. This also gives us

the usual benefits of keeping dependencies to a minimum.



### Q: Can I generalize X, then I can do Y with minimal modifications?



Code generalization often makes code harder to read. This is not intended to be

a library in the sense that you import an agent and inject customized components

to do research. Instead it is designed to be easy to customize after forking. So

rather than be everything for everyone, we aimed to keep things minimal. Then

users can fork and generalize in the directions they specifically care about.



### Q: Why Gym instead of Xitari?



Most DeepMind papers with experiments on Atari published results on

[Xitari](http://github.com/deepmind/xitari), a fork of the

[Arcade Learning Environment (ALE)](http://github.com/mgbellemare/Arcade-Learning-Environment).

The learning performance of agents in DQN Zoo were also verified on Xitari.

However since [Gym](http://github.com/openai/gym) and the ALE are more widely

used we have chosen to open source DQN Zoo using Gym. This does introduce

another source of differences, though the settings for the Gym Atari

environments have been chosen so they behave as similar as possible to Xitari.



## Contributing



Note we are currently not accepting contributions. See

[`CONTRIBUTING.md`](CONTRIBUTING.md) for details.



## Citing DQN Zoo



If you use DQN Zoo in your research, please cite the papers corresponding to the

agents used and this repository:



```

@software{dqnzoo2020github,

  title = {{DQN} {Zoo}: Reference implementations of {DQN}-based agents},

  author = {John Quan and Georg Ostrovski},

  url = {http://github.com/deepmind/dqn_zoo},

  version = {1.2.0},

  year = {2020},

}

```