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| author | CoprDistGit <infra@openeuler.org> | 2023-05-18 07:17:40 +0000 |
|---|---|---|
| committer | CoprDistGit <infra@openeuler.org> | 2023-05-18 07:17:40 +0000 |
| commit | 31efaedcc15e500c3c20d410b3c40246e4d45ba9 (patch) | |
| tree | cb46028e4be8e0b2d0d5f541941722a70415199c | |
| parent | 92aa76640ec4c913d3036f60283ee2614e10b6ba (diff) | |
automatic import of python-ai-traineree
| -rw-r--r-- | .gitignore | 1 | ||||
| -rw-r--r-- | python-ai-traineree.spec | 548 | ||||
| -rw-r--r-- | sources | 1 |
3 files changed, 550 insertions, 0 deletions
@@ -0,0 +1 @@ +/ai-traineree-0.5.2.tar.gz diff --git a/python-ai-traineree.spec b/python-ai-traineree.spec new file mode 100644 index 0000000..179cc57 --- /dev/null +++ b/python-ai-traineree.spec @@ -0,0 +1,548 @@ +%global _empty_manifest_terminate_build 0 +Name: python-ai-traineree +Version: 0.5.2 +Release: 1 +Summary: Yet another zoo of (Deep) Reinforcment Learning methods in Python using PyTorch +License: Apache Software License +URL: https://github.com/laszukdawid/ai-traineree +Source0: https://mirrors.nju.edu.cn/pypi/web/packages/22/7c/19594210c0374278e9b1b5e5f0628cffe80505bdde448f3d19f957daac59/ai-traineree-0.5.2.tar.gz +BuildArch: noarch + +Requires: python3-numpy +Requires: python3-torch +Requires: python3-jsons +Requires: python3-gym +Requires: python3-black +Requires: python3-isort +Requires: python3-pycodestyle +Requires: python3-sphinx +Requires: python3-sphinx-rtd-theme +Requires: python3-matplotlib +Requires: python3-tensorboard +Requires: python3-gym +Requires: python3-gym[all] +Requires: python3-mlagents +Requires: python3-neptune-client +Requires: python3-psutil +Requires: python3-pettingzoo[other] +Requires: python3-matplotlib +Requires: python3-tensorboard +Requires: python3-mock +Requires: python3-pytest +Requires: python3-pytest-cov +Requires: python3-pytest-mock +Requires: python3-flake8 +Requires: python3-gym[box2d] +Requires: python3-pettingzoo[other,sisl] + +%description +# ai-traineree +[](https://app.travis-ci.com/laszukdawid/ai-traineree) +[](https://ai-traineree.readthedocs.io/) +[](https://codecov.io/gh/laszukdawid/ai-traineree) +[](https://www.codacy.com/gh/laszukdawid/ai-traineree/dashboard?utm_source=github.com&utm_medium=referral&utm_content=laszukdawid/ai-traineree&utm_campaign=Badge_Grade) +[](https://discord.gg/n4bN7ZHn) +[](https://zenodo.org/badge/latestdoi/267484000) + +The intention is to have a zoo of Deep Reinforcment Learning methods and showcasing their application on some environments. + +Read more in the doc: [ReadTheDocs AI-Traineree](https://ai-traineree.readthedocs.io/). + + + + +## Why another? + +The main reason is the implemention philosophy. +We strongly believe that agents should be emerged in the environment and not the other way round. +Majority of the popular implementations pass environment instance to the agent as if the agent was the focus point. +This might ease implementation of some algorithms but it isn't representative of the world; +agents want to control the environment but that doesn't mean they can/should. + +That, and using PyTorch instead of Tensorflow or JAX. + +## Quick start + +To get started with training your RL agent you need three things: an agent, an environment and a runner. Let's say you want to train a DQN agent on OpenAI CartPole-v1: +```python +from ai_traineree.agents.dqn import DQNAgent +from ai_traineree.runners.env_runner import EnvRunner +from ai_traineree.tasks import GymTask + +task = GymTask('CartPole-v1') +agent = DQNAgent(task.obs_space, task.action_space) +env_runner = EnvRunner(task, agent) + +scores = env_runner.run() +``` + +or execute one of provided examples +> \$ python -m examples.cart_dqn + +That's it. + +## Installation + +### PyPi (recommended) + +The quickest way to install package is through `pip`. + +> \$ pip install ai-traineree + +### Git repository clone + +As usual with Python, the expectation is to have own virtual environment and then pip install requirements. For example, +```bash +> python -m venv .venv +> git clone git@github.com:laszukdawid/ai-traineree.git +> source .venv/bin/activate +> python setup.py install +``` + +## Current state + +### Playing gym +One way to improve learning speed is to simply show them how to play or, more researchy/creepy, provide a proper seed. +This isn't a general rule, since some algorithms train better without any human interaction, but since you're on GitHub... that's unlikely your case. +Currently there's a script [`interact.py`](scripts/interact.py) which uses OpenAI Gym's play API to record moves and AI Traineree to store them +in a buffer. Such buffers can be loaded by agents on initiation. + +This is just a beginning and there will be more work on these interactions. + +*Requirement*: Install `pygame`. + +### Agents + +| Short | Progress | Link | Full name | Doc | +| ------- | --------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------- | ------------------------------------------------------------------------ | +| DQN | [Implemented](ai_traineree/agents/dqn.py) | [DeepMind](https://deepmind.com/research/publications/human-level-control-through-deep-reinforcement-learning) | Deep Q-learning Network | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#dqn) | +| DDPG | [Implemented](ai_traineree/agents/ddpg.py) | [arXiv](https://arxiv.org/abs/1509.02971) | Deep Deterministic Policy Gradient | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ddpg) | +| D4PG | [Implemented](ai_traineree/agents/d4pg.py) | [arXiv](https://arxiv.org/abs/1804.08617) | Distributed Distributional Deterministic Policy Gradients | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#d4pg) | +| TD3 | [Implemented](ai_traineree/agents/td3.py) | [arXiv](https://arxiv.org/abs/1802.09477) | Twine Delayed Deep Deterministic policy gradient | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#td3) | +| PPO | [Implemented](ai_traineree/agents/ppo.py) | [arXiv](https://arxiv.org/abs/1707.06347) | Proximal Policy Optimization | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ppo) | +| SAC | [Implemented](ai_traineree/agents/sac.py) | [arXiv](https://arxiv.org/abs/1801.01290) | Soft Actor Critic | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#sac) | +| TRPO | | [arXiv](https://arxiv.org/abs/1502.05477) | Trust Region Policy Optimization | +| RAINBOW | [Implemented](ai_traineree/agents/rainbow.py) | [arXiv](https://arxiv.org/abs/1710.02298) | DQN with a few improvements | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#rainbow) | + +### Multi agents + +We provide both Multi Agents agents entities and means to execute them against supported (below) environements. +However, that doesn't mean one can be used without the other. + +| Short | Progress | Link | Full name | Doc | +| ------ | ------------------------------------------------- | ----------------------------------------- | ---------------------- | ---------------------------------------------------------------------------- | +| IQL | [Implemented](ai_traineree/multi_agent/iql.py) | | Independent Q-Learners | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#iql) | +| MADDPG | [Implemented](ai_traineree/multi_agent/maddpg.py) | [arXiv](https://arxiv.org/abs/1706.02275) | Multi agent DDPG | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#maddpg) | + +### Loggers + +Supports using Tensorboard (via PyTorch's [SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html)) and [Neptune](https://neptune.ai) to display metrics. Wrappers are provided as `TensorboardLogger` and `NeptuneLogger`. + +*Note*: In order to use Neptune one needs to install [neptune-client](https://pypi.org/project/neptune-client/) (`pip install neptune-client`). + +### Environments + +| Name | Progress | Link | +| -------------------- | ------------------ | -------------------------------------------------------------------------------------------- | +| OpenAI Gym - Classic | Done | +| OpenAI Gym - Atari | Done | +| OpenAI Gym - MuJoCo | Not interested. | +| PettingZoo | Initial support | [Page](https://www.pettingzoo.ml/) / [GitHub](https://github.com/PettingZoo-Team/PettingZoo) | +| Unity ML | Somehow supported. | [Page](https://unity3d.com/machine-learning) | +| MAME Linux emulator | Interested. | [Official page](https://www.mamedev.org/) | + +### Development + +We are open to any contributions. If you want to contribute but don't know what then feel free to reach out (see Contact below). +The best way to start is through updating documentation and adding tutorials. +In addition there are many other things that we know of which need improvement but also plenty that we don't know of. + +Setting up development environment requires installing `dev` and `test` extra packages. +The `dev` extras are for mainly for linting and formatting, and the `test` is for running tests. +We recommend using `pip` so to install everything requires for development run + +```bash +$ pip install -e .[dev,test] +``` + +Once installed, please configure your IDE to use `black` as formatter, `pycodestyle` as linter, +and `isort` for sorting imports. All these are included in the `dev` extra packages. + +### Contact + +Should we focus on something specificallly? Let us know by opening a feature request [GitHub issue](https://github.com/laszukdawid/ai-traineree/issues) or contacting through [ai-traineree@dawid.lasz.uk](mailto:ai-traineree@dawid.lasz.uk). + +## Citing project + +```latex +@misc{ai-traineree, + author = {Laszuk, Dawid}, + title = {AI Traineree: Reinforcement learning toolset}, + year = {2020}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/laszukdawid/ai-traineree}}, +} +``` + + + + +%package -n python3-ai-traineree +Summary: Yet another zoo of (Deep) Reinforcment Learning methods in Python using PyTorch +Provides: python-ai-traineree +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-ai-traineree +# ai-traineree +[](https://app.travis-ci.com/laszukdawid/ai-traineree) +[](https://ai-traineree.readthedocs.io/) +[](https://codecov.io/gh/laszukdawid/ai-traineree) +[](https://www.codacy.com/gh/laszukdawid/ai-traineree/dashboard?utm_source=github.com&utm_medium=referral&utm_content=laszukdawid/ai-traineree&utm_campaign=Badge_Grade) +[](https://discord.gg/n4bN7ZHn) +[](https://zenodo.org/badge/latestdoi/267484000) + +The intention is to have a zoo of Deep Reinforcment Learning methods and showcasing their application on some environments. + +Read more in the doc: [ReadTheDocs AI-Traineree](https://ai-traineree.readthedocs.io/). + + + + +## Why another? + +The main reason is the implemention philosophy. +We strongly believe that agents should be emerged in the environment and not the other way round. +Majority of the popular implementations pass environment instance to the agent as if the agent was the focus point. +This might ease implementation of some algorithms but it isn't representative of the world; +agents want to control the environment but that doesn't mean they can/should. + +That, and using PyTorch instead of Tensorflow or JAX. + +## Quick start + +To get started with training your RL agent you need three things: an agent, an environment and a runner. Let's say you want to train a DQN agent on OpenAI CartPole-v1: +```python +from ai_traineree.agents.dqn import DQNAgent +from ai_traineree.runners.env_runner import EnvRunner +from ai_traineree.tasks import GymTask + +task = GymTask('CartPole-v1') +agent = DQNAgent(task.obs_space, task.action_space) +env_runner = EnvRunner(task, agent) + +scores = env_runner.run() +``` + +or execute one of provided examples +> \$ python -m examples.cart_dqn + +That's it. + +## Installation + +### PyPi (recommended) + +The quickest way to install package is through `pip`. + +> \$ pip install ai-traineree + +### Git repository clone + +As usual with Python, the expectation is to have own virtual environment and then pip install requirements. For example, +```bash +> python -m venv .venv +> git clone git@github.com:laszukdawid/ai-traineree.git +> source .venv/bin/activate +> python setup.py install +``` + +## Current state + +### Playing gym +One way to improve learning speed is to simply show them how to play or, more researchy/creepy, provide a proper seed. +This isn't a general rule, since some algorithms train better without any human interaction, but since you're on GitHub... that's unlikely your case. +Currently there's a script [`interact.py`](scripts/interact.py) which uses OpenAI Gym's play API to record moves and AI Traineree to store them +in a buffer. Such buffers can be loaded by agents on initiation. + +This is just a beginning and there will be more work on these interactions. + +*Requirement*: Install `pygame`. + +### Agents + +| Short | Progress | Link | Full name | Doc | +| ------- | --------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------- | ------------------------------------------------------------------------ | +| DQN | [Implemented](ai_traineree/agents/dqn.py) | [DeepMind](https://deepmind.com/research/publications/human-level-control-through-deep-reinforcement-learning) | Deep Q-learning Network | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#dqn) | +| DDPG | [Implemented](ai_traineree/agents/ddpg.py) | [arXiv](https://arxiv.org/abs/1509.02971) | Deep Deterministic Policy Gradient | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ddpg) | +| D4PG | [Implemented](ai_traineree/agents/d4pg.py) | [arXiv](https://arxiv.org/abs/1804.08617) | Distributed Distributional Deterministic Policy Gradients | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#d4pg) | +| TD3 | [Implemented](ai_traineree/agents/td3.py) | [arXiv](https://arxiv.org/abs/1802.09477) | Twine Delayed Deep Deterministic policy gradient | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#td3) | +| PPO | [Implemented](ai_traineree/agents/ppo.py) | [arXiv](https://arxiv.org/abs/1707.06347) | Proximal Policy Optimization | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ppo) | +| SAC | [Implemented](ai_traineree/agents/sac.py) | [arXiv](https://arxiv.org/abs/1801.01290) | Soft Actor Critic | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#sac) | +| TRPO | | [arXiv](https://arxiv.org/abs/1502.05477) | Trust Region Policy Optimization | +| RAINBOW | [Implemented](ai_traineree/agents/rainbow.py) | [arXiv](https://arxiv.org/abs/1710.02298) | DQN with a few improvements | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#rainbow) | + +### Multi agents + +We provide both Multi Agents agents entities and means to execute them against supported (below) environements. +However, that doesn't mean one can be used without the other. + +| Short | Progress | Link | Full name | Doc | +| ------ | ------------------------------------------------- | ----------------------------------------- | ---------------------- | ---------------------------------------------------------------------------- | +| IQL | [Implemented](ai_traineree/multi_agent/iql.py) | | Independent Q-Learners | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#iql) | +| MADDPG | [Implemented](ai_traineree/multi_agent/maddpg.py) | [arXiv](https://arxiv.org/abs/1706.02275) | Multi agent DDPG | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#maddpg) | + +### Loggers + +Supports using Tensorboard (via PyTorch's [SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html)) and [Neptune](https://neptune.ai) to display metrics. Wrappers are provided as `TensorboardLogger` and `NeptuneLogger`. + +*Note*: In order to use Neptune one needs to install [neptune-client](https://pypi.org/project/neptune-client/) (`pip install neptune-client`). + +### Environments + +| Name | Progress | Link | +| -------------------- | ------------------ | -------------------------------------------------------------------------------------------- | +| OpenAI Gym - Classic | Done | +| OpenAI Gym - Atari | Done | +| OpenAI Gym - MuJoCo | Not interested. | +| PettingZoo | Initial support | [Page](https://www.pettingzoo.ml/) / [GitHub](https://github.com/PettingZoo-Team/PettingZoo) | +| Unity ML | Somehow supported. | [Page](https://unity3d.com/machine-learning) | +| MAME Linux emulator | Interested. | [Official page](https://www.mamedev.org/) | + +### Development + +We are open to any contributions. If you want to contribute but don't know what then feel free to reach out (see Contact below). +The best way to start is through updating documentation and adding tutorials. +In addition there are many other things that we know of which need improvement but also plenty that we don't know of. + +Setting up development environment requires installing `dev` and `test` extra packages. +The `dev` extras are for mainly for linting and formatting, and the `test` is for running tests. +We recommend using `pip` so to install everything requires for development run + +```bash +$ pip install -e .[dev,test] +``` + +Once installed, please configure your IDE to use `black` as formatter, `pycodestyle` as linter, +and `isort` for sorting imports. All these are included in the `dev` extra packages. + +### Contact + +Should we focus on something specificallly? Let us know by opening a feature request [GitHub issue](https://github.com/laszukdawid/ai-traineree/issues) or contacting through [ai-traineree@dawid.lasz.uk](mailto:ai-traineree@dawid.lasz.uk). + +## Citing project + +```latex +@misc{ai-traineree, + author = {Laszuk, Dawid}, + title = {AI Traineree: Reinforcement learning toolset}, + year = {2020}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/laszukdawid/ai-traineree}}, +} +``` + + + + +%package help +Summary: Development documents and examples for ai-traineree +Provides: python3-ai-traineree-doc +%description help +# ai-traineree +[](https://app.travis-ci.com/laszukdawid/ai-traineree) +[](https://ai-traineree.readthedocs.io/) +[](https://codecov.io/gh/laszukdawid/ai-traineree) +[](https://www.codacy.com/gh/laszukdawid/ai-traineree/dashboard?utm_source=github.com&utm_medium=referral&utm_content=laszukdawid/ai-traineree&utm_campaign=Badge_Grade) +[](https://discord.gg/n4bN7ZHn) +[](https://zenodo.org/badge/latestdoi/267484000) + +The intention is to have a zoo of Deep Reinforcment Learning methods and showcasing their application on some environments. + +Read more in the doc: [ReadTheDocs AI-Traineree](https://ai-traineree.readthedocs.io/). + + + + +## Why another? + +The main reason is the implemention philosophy. +We strongly believe that agents should be emerged in the environment and not the other way round. +Majority of the popular implementations pass environment instance to the agent as if the agent was the focus point. +This might ease implementation of some algorithms but it isn't representative of the world; +agents want to control the environment but that doesn't mean they can/should. + +That, and using PyTorch instead of Tensorflow or JAX. + +## Quick start + +To get started with training your RL agent you need three things: an agent, an environment and a runner. Let's say you want to train a DQN agent on OpenAI CartPole-v1: +```python +from ai_traineree.agents.dqn import DQNAgent +from ai_traineree.runners.env_runner import EnvRunner +from ai_traineree.tasks import GymTask + +task = GymTask('CartPole-v1') +agent = DQNAgent(task.obs_space, task.action_space) +env_runner = EnvRunner(task, agent) + +scores = env_runner.run() +``` + +or execute one of provided examples +> \$ python -m examples.cart_dqn + +That's it. + +## Installation + +### PyPi (recommended) + +The quickest way to install package is through `pip`. + +> \$ pip install ai-traineree + +### Git repository clone + +As usual with Python, the expectation is to have own virtual environment and then pip install requirements. For example, +```bash +> python -m venv .venv +> git clone git@github.com:laszukdawid/ai-traineree.git +> source .venv/bin/activate +> python setup.py install +``` + +## Current state + +### Playing gym +One way to improve learning speed is to simply show them how to play or, more researchy/creepy, provide a proper seed. +This isn't a general rule, since some algorithms train better without any human interaction, but since you're on GitHub... that's unlikely your case. +Currently there's a script [`interact.py`](scripts/interact.py) which uses OpenAI Gym's play API to record moves and AI Traineree to store them +in a buffer. Such buffers can be loaded by agents on initiation. + +This is just a beginning and there will be more work on these interactions. + +*Requirement*: Install `pygame`. + +### Agents + +| Short | Progress | Link | Full name | Doc | +| ------- | --------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------- | ------------------------------------------------------------------------ | +| DQN | [Implemented](ai_traineree/agents/dqn.py) | [DeepMind](https://deepmind.com/research/publications/human-level-control-through-deep-reinforcement-learning) | Deep Q-learning Network | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#dqn) | +| DDPG | [Implemented](ai_traineree/agents/ddpg.py) | [arXiv](https://arxiv.org/abs/1509.02971) | Deep Deterministic Policy Gradient | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ddpg) | +| D4PG | [Implemented](ai_traineree/agents/d4pg.py) | [arXiv](https://arxiv.org/abs/1804.08617) | Distributed Distributional Deterministic Policy Gradients | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#d4pg) | +| TD3 | [Implemented](ai_traineree/agents/td3.py) | [arXiv](https://arxiv.org/abs/1802.09477) | Twine Delayed Deep Deterministic policy gradient | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#td3) | +| PPO | [Implemented](ai_traineree/agents/ppo.py) | [arXiv](https://arxiv.org/abs/1707.06347) | Proximal Policy Optimization | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#ppo) | +| SAC | [Implemented](ai_traineree/agents/sac.py) | [arXiv](https://arxiv.org/abs/1801.01290) | Soft Actor Critic | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#sac) | +| TRPO | | [arXiv](https://arxiv.org/abs/1502.05477) | Trust Region Policy Optimization | +| RAINBOW | [Implemented](ai_traineree/agents/rainbow.py) | [arXiv](https://arxiv.org/abs/1710.02298) | DQN with a few improvements | [Doc](https://ai-traineree.readthedocs.io/en/latest/agents.html#rainbow) | + +### Multi agents + +We provide both Multi Agents agents entities and means to execute them against supported (below) environements. +However, that doesn't mean one can be used without the other. + +| Short | Progress | Link | Full name | Doc | +| ------ | ------------------------------------------------- | ----------------------------------------- | ---------------------- | ---------------------------------------------------------------------------- | +| IQL | [Implemented](ai_traineree/multi_agent/iql.py) | | Independent Q-Learners | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#iql) | +| MADDPG | [Implemented](ai_traineree/multi_agent/maddpg.py) | [arXiv](https://arxiv.org/abs/1706.02275) | Multi agent DDPG | [Doc](https://ai-traineree.readthedocs.io/en/latest/multi_agent.html#maddpg) | + +### Loggers + +Supports using Tensorboard (via PyTorch's [SummaryWriter](https://pytorch.org/docs/stable/tensorboard.html)) and [Neptune](https://neptune.ai) to display metrics. Wrappers are provided as `TensorboardLogger` and `NeptuneLogger`. + +*Note*: In order to use Neptune one needs to install [neptune-client](https://pypi.org/project/neptune-client/) (`pip install neptune-client`). + +### Environments + +| Name | Progress | Link | +| -------------------- | ------------------ | -------------------------------------------------------------------------------------------- | +| OpenAI Gym - Classic | Done | +| OpenAI Gym - Atari | Done | +| OpenAI Gym - MuJoCo | Not interested. | +| PettingZoo | Initial support | [Page](https://www.pettingzoo.ml/) / [GitHub](https://github.com/PettingZoo-Team/PettingZoo) | +| Unity ML | Somehow supported. | [Page](https://unity3d.com/machine-learning) | +| MAME Linux emulator | Interested. | [Official page](https://www.mamedev.org/) | + +### Development + +We are open to any contributions. If you want to contribute but don't know what then feel free to reach out (see Contact below). +The best way to start is through updating documentation and adding tutorials. +In addition there are many other things that we know of which need improvement but also plenty that we don't know of. + +Setting up development environment requires installing `dev` and `test` extra packages. +The `dev` extras are for mainly for linting and formatting, and the `test` is for running tests. +We recommend using `pip` so to install everything requires for development run + +```bash +$ pip install -e .[dev,test] +``` + +Once installed, please configure your IDE to use `black` as formatter, `pycodestyle` as linter, +and `isort` for sorting imports. All these are included in the `dev` extra packages. + +### Contact + +Should we focus on something specificallly? Let us know by opening a feature request [GitHub issue](https://github.com/laszukdawid/ai-traineree/issues) or contacting through [ai-traineree@dawid.lasz.uk](mailto:ai-traineree@dawid.lasz.uk). + +## Citing project + +```latex +@misc{ai-traineree, + author = {Laszuk, Dawid}, + title = {AI Traineree: Reinforcement learning toolset}, + year = {2020}, + publisher = {GitHub}, + journal = {GitHub repository}, + howpublished = {\url{https://github.com/laszukdawid/ai-traineree}}, +} +``` + + + + +%prep +%autosetup -n ai-traineree-0.5.2 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-ai-traineree -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Thu May 18 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5.2-1 +- Package Spec generated @@ -0,0 +1 @@ +94c07376a7a1e2dd37b6fd53f5967c68 ai-traineree-0.5.2.tar.gz |