| Name | jumanji JSON |
| Version |
1.1.0
JSON |
| download |
| home_page | None |
| Summary | A diverse suite of scalable reinforcement learning environments in JAX |
| upload_time | 2024-11-22 16:29:37 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | >=3.10 |
| license | Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. "License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. "Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. "Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. "You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License. "Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. "Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. "Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). "Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution." "Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets "[]" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2022 InstaDeep Ltd Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
reinforcement-learning
python
jax
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
<p align="center">
<a href="docs/img/jumanji_logo.png">
<img src="docs/img/jumanji_logo.png" alt="Jumanji logo" width="50%"/>
</a>
</p>
[](https://www.python.org/doc/versions/)
[](https://badge.fury.io/py/jumanji)
[](https://github.com/instadeepai/jumanji/actions/workflows/tests_linters.yml)
[](https://github.com/astral-sh/ruff)
[](http://mypy-lang.org/)
[](https://opensource.org/licenses/Apache-2.0)
[](https://huggingface.co/InstaDeepAI)
[**Environments**](#environments)
| [**Installation**](#install)
| [**Quickstart**](#quickstart)
| [**Training**](#training)
| [**Citation**](#citing)
| [**Docs**](https://instadeepai.github.io/jumanji)
---
<div class="collage">
<div class="row" align="center">
<img src="docs/env_anim/bin_pack.gif" alt="BinPack" width="16%">
<img src="docs/env_anim/cleaner.gif" alt="Cleaner" width="16%">
<img src="docs/env_anim/connector.gif" alt="Connector" width="16%">
<img src="docs/env_anim/cvrp.gif" alt="CVRP" width="16%">
<img src="docs/env_anim/flat_pack.gif" alt="FlatPack" width="16%">
<img src="docs/env_anim/game_2048.gif" alt="Game2048" width="16%">
</div>
<div class="row" align="center">
<img src="docs/env_anim/graph_coloring.gif" alt="GraphColoring" width="16%">
<img src="docs/env_anim/job_shop.gif" alt="JobShop" width="16%">
<img src="docs/env_anim/knapsack.gif" alt="Knapsack" width="16%">
<img src="docs/env_anim/maze.gif" alt="Maze" width="16%">
<img src="docs/env_anim/minesweeper.gif" alt="Minesweeper" width="16%">
<img src="docs/env_anim/mmst.gif" alt="MMST" width="16%">
</div>
<div class="row" align="center">
<img src="docs/env_anim/multi_cvrp.gif" alt="MultiCVRP" width="16%">
<img src="docs/env_anim/pac_man.gif" alt="PacMan" width="12.9%">
<img src="docs/env_anim/robot_warehouse.gif" alt="RobotWarehouse" width="16%">
<img src="docs/env_anim/rubiks_cube.gif" alt="RubiksCube" width="16%">
<img src="docs/env_anim/sliding_tile_puzzle.gif" alt="SlidingTilePuzzle" width="16%">
<img src="docs/env_anim/snake.gif" alt="Snake" width="16%">
</div>
<div class="row" align="center">
<img src="docs/env_anim/sokoban.gif" alt="RobotWarehouse" width="16%">
<img src="docs/env_anim/sudoku.gif" alt="Sudoku" width="16%">
<img src="docs/env_anim/tetris.gif" alt="Tetris" width="16%">
<img src="docs/env_anim/tsp.gif" alt="Tetris" width="16%">
<img src="docs/env_anim/lbf.gif" alt="Level-Based Foraging" width="16%">
</div>
</div>
## Jumanji @ ICLR 2024
Jumanji has been accepted at [ICLR 2024](https://iclr.cc/), check out our [research paper](https://arxiv.org/abs/2306.09884).
## Welcome to the Jungle! π΄
Jumanji is a diverse suite of scalable reinforcement learning environments written in JAX. It now features 22 environments!
Jumanji is helping pioneer a new wave of hardware-accelerated research and development in the
field of RL. Jumanji's high-speed environments enable faster iteration and large-scale
experimentation while simultaneously reducing complexity. Originating in the research team at
[InstaDeep](https://www.instadeep.com/), Jumanji is now developed jointly with the open-source
community. To join us in these efforts, reach out, raise issues and read our
[contribution guidelines](https://github.com/instadeepai/jumanji/blob/main/CONTRIBUTING.md) or just
[star](https://github.com/instadeepai/jumanji) π to stay up to date with the latest developments!
### Goals π
1. Provide a simple, well-tested API for JAX-based environments.
2. Make research in RL more accessible.
3. Facilitate the research on RL for problems in the industry and help close the gap between
research and industrial applications.
4. Provide environments whose difficulty can be scaled to be arbitrarily hard.
### Overview π¦
- π₯ **Environment API**: core abstractions for JAX-based environments.
- πΉοΈ **Environment Suite**: a collection of RL environments ranging from simple games to NP-hard
combinatorial problems.
- π¬ **Wrappers**: easily connect to your favourite RL frameworks and libraries such as
[Acme](https://github.com/deepmind/acme),
[Stable Baselines3](https://github.com/DLR-RM/stable-baselines3),
[RLlib](https://docs.ray.io/en/latest/rllib/index.html), [Gymnasium](https://github.com/Farama-Foundation/Gymnasium)
and [DeepMind-Env](https://github.com/deepmind/dm_env) through our `dm_env` and `gym` wrappers.
- π **Examples**: guides to facilitate Jumanji's adoption and highlight the added value of
JAX-based environments.
- ποΈ **Training:** example agents that can be used as inspiration for the agents one may implement
in their research.
<h2 name="environments" id="environments">Environments π</h2>
Jumanji provides a diverse range of environments ranging from simple games to NP-hard combinatorial
problems.
| Environment | Category | Registered Version(s) | Source | Description |
|------------------------------------------|----------|------------------------------------------------------|--------------------------------------------------------------------------------------------------|------------------------------------------------------------------------|
| π’ Game2048 | Logic | `Game2048-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/game_2048/) | [doc](https://instadeepai.github.io/jumanji/environments/game_2048/) |
| π¨ GraphColoring | Logic | `GraphColoring-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/graph_coloring/) | [doc](https://instadeepai.github.io/jumanji/environments/graph_coloring/) |
| π£ Minesweeper | Logic | `Minesweeper-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/minesweeper/) | [doc](https://instadeepai.github.io/jumanji/environments/minesweeper/) |
| π² RubiksCube | Logic | `RubiksCube-v0`<br/>`RubiksCube-partly-scrambled-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/rubiks_cube/) | [doc](https://instadeepai.github.io/jumanji/environments/rubiks_cube/) |
| π SlidingTilePuzzle | Logic | `SlidingTilePuzzle-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/sliding_tile_puzzle/) | [doc](https://instadeepai.github.io/jumanji/environments/sliding_tile_puzzle/) |
| βοΈ Sudoku | Logic | `Sudoku-v0` <br/>`Sudoku-very-easy-v0`| [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/sudoku/) | [doc](https://instadeepai.github.io/jumanji/environments/sudoku/) |
| π¦ BinPack (3D BinPacking Problem) | Packing | `BinPack-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/bin_pack/) | [doc](https://instadeepai.github.io/jumanji/environments/bin_pack/) |
| 𧩠FlatPack (2D Grid Filling Problem) | Packing | `FlatPack-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/flat_pack/) | [doc](https://instadeepai.github.io/jumanji/environments/flat_pack/) |
| π JobShop (Job Shop Scheduling Problem) | Packing | `JobShop-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/job_shop/) | [doc](https://instadeepai.github.io/jumanji/environments/job_shop/) |
| π Knapsack | Packing | `Knapsack-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/knapsack/) | [doc](https://instadeepai.github.io/jumanji/environments/knapsack/) |
| β Tetris | Packing | `Tetris-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/tetris/) | [doc](https://instadeepai.github.io/jumanji/environments/tetris/) |
| π§Ή Cleaner | Routing | `Cleaner-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/cleaner/) | [doc](https://instadeepai.github.io/jumanji/environments/cleaner/) |
| :link: Connector | Routing | `Connector-v2` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/connector/) | [doc](https://instadeepai.github.io/jumanji/environments/connector/) |
| π CVRP (Capacitated Vehicle Routing Problem) | Routing | `CVRP-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/cvrp/) | [doc](https://instadeepai.github.io/jumanji/environments/cvrp/) |
| π MultiCVRP (Multi-Agent Capacitated Vehicle Routing Problem) | Routing | `MultiCVRP-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/multi_cvrp/) | [doc](https://instadeepai.github.io/jumanji/environments/multi_cvrp/) |
| :mag: Maze | Routing | `Maze-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/maze/) | [doc](https://instadeepai.github.io/jumanji/environments/maze/) |
| :robot: RobotWarehouse | Routing | `RobotWarehouse-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/robot_warehouse/) | [doc](https://instadeepai.github.io/jumanji/environments/robot_warehouse/) |
| π Snake | Routing | `Snake-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/snake/) | [doc](https://instadeepai.github.io/jumanji/environments/snake/) |
| π¬ TSP (Travelling Salesman Problem) | Routing | `TSP-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/tsp/) | [doc](https://instadeepai.github.io/jumanji/environments/tsp/) |
| Multi Minimum Spanning Tree Problem | Routing | `MMST-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/mmst) | [doc](https://instadeepai.github.io/jumanji/environments/mmst/) |
| α§β’β’β’α£β’β’ PacMan | Routing | `PacMan-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/pac_man/) | [doc](https://instadeepai.github.io/jumanji/environments/pac_man/)
| πΎ Sokoban | Routing | `Sokoban-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/sokoban/) | [doc](https://instadeepai.github.io/jumanji/environments/sokoban/) |
| π Level-Based Foraging | Routing | `LevelBasedForaging-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/lbf/) | [doc](https://instadeepai.github.io/jumanji/environments/lbf/) |
<h2 name="install" id="install">Installation π¬</h2>
You can install the latest release of Jumanji from PyPI:
```bash
pip install -U jumanji
```
Alternatively, you can install the latest development version directly from GitHub:
```bash
pip install git+https://github.com/instadeepai/jumanji.git
```
Jumanji has been tested on Python 3.10, 3.11 and 3.12.
Note that because the installation of JAX differs depending on your hardware accelerator,
we advise users to explicitly install the correct JAX version (see the
[official installation guide](https://github.com/google/jax#installation)).
**Rendering:** Matplotlib is used for rendering all the environments. To visualize the environments
you will need a GUI backend. For example, on Linux, you can install Tk via:
`apt-get install python3-tk`, or using conda: `conda install tk`. Check out
[Matplotlib backends](https://matplotlib.org/stable/users/explain/backends.html) for a list of
backends you can use.
<h2 name="quickstart" id="quickstart">Quickstart β‘</h2>
RL practitioners will find Jumanji's interface familiar as it combines the widely adopted
[OpenAI Gym](https://github.com/openai/gym) and
[DeepMind Environment](https://github.com/deepmind/dm_env) interfaces. From OpenAI Gym, we adopted
the idea of a `registry` and the `render` method, while our `TimeStep` structure is inspired by
DeepMind Environment.
### Basic Usage π§βπ»
```python
import jax
import jumanji
# Instantiate a Jumanji environment using the registry
env = jumanji.make('Snake-v1')
# Reset your (jit-able) environment
key = jax.random.PRNGKey(0)
state, timestep = jax.jit(env.reset)(key)
# (Optional) Render the env state
env.render(state)
# Interact with the (jit-able) environment
action = env.action_spec.generate_value() # Action selection (dummy value here)
state, timestep = jax.jit(env.step)(state, action) # Take a step and observe the next state and time step
```
- `state` represents the internal state of the environment: it contains all the information required
to take a step when executing an action. This should **not** be confused with the `observation`
contained in the `timestep`, which is the information perceived by the agent.
- `timestep` is a dataclass containing `step_type`, `reward`, `discount`, `observation` and
`extras`. This structure is similar to
[`dm_env.TimeStep`](https://github.com/deepmind/dm_env/blob/master/docs/index.md) except for the
`extras` field that was added to allow users to log environments metrics that are neither part of
the agent's observation nor part of the environment's internal state.
### Advanced Usage π§βπ¬
Being written in JAX, Jumanji's environments benefit from many of its features including
automatic vectorization/parallelization (`jax.vmap`, `jax.pmap`) and JIT-compilation (`jax.jit`),
which can be composed arbitrarily.
We provide an example of a more advanced usage in the
[advanced usage guide](https://instadeepai.github.io/jumanji/guides/advanced_usage/).
### Registry and Versioning π
Like OpenAI Gym, Jumanji keeps a strict versioning of its environments for reproducibility reasons.
We maintain a registry of standard environments with their configuration.
For each environment, a version suffix is appended, e.g. `Snake-v1`.
When changes are made to environments that might impact learning results,
the version number is incremented by one to prevent potential confusion.
For a full list of registered versions of each environment, check out
[the documentation](https://instadeepai.github.io/jumanji/environments/tsp/).
<h2 name="training" id="training">Training ποΈ</h2>
To showcase how to train RL agents on Jumanji environments, we provide a random agent and a vanilla
actor-critic (A2C) agent. These agents can be found in
[jumanji/training/](https://github.com/instadeepai/jumanji/tree/main/jumanji/training/).
Because the environment framework in Jumanji is so flexible, it allows pretty much any problem to
be implemented as a Jumanji environment, giving rise to very diverse observations. For this reason,
environment-specific networks are required to capture the symmetries of each environment.
Alongside the A2C agent implementation, we provide examples of such environment-specific
actor-critic networks in
[jumanji/training/networks](https://github.com/instadeepai/jumanji/tree/main/jumanji/training/networks/).
> β οΈ The example agents in `jumanji/training` are **only** meant to serve as inspiration for how one
> can implement an agent. Jumanji is first and foremost a library of environments - as such, the
> agents and networks will **not** be maintained to a production standard.
For more information on how to use the example agents, see the
[training guide](https://instadeepai.github.io/jumanji/guides/training/).
## Contributing π€
Contributions are welcome! See our issue tracker for
[good first issues](https://github.com/instadeepai/jumanji/labels/good%20first%20issue). Please read
our [contributing guidelines](https://github.com/instadeepai/jumanji/blob/main/CONTRIBUTING.md) for
details on how to submit pull requests, our Contributor License Agreement, and community guidelines.
<h2 name="citing" id="citing">Citing Jumanji βοΈ</h2>
If you use Jumanji in your work, please cite the library using:
```
@misc{bonnet2024jumanji,
title={Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX},
author={ClΓ©ment Bonnet and Daniel Luo and Donal Byrne and Shikha Surana and Sasha Abramowitz and Paul Duckworth and Vincent Coyette and Laurence I. Midgley and Elshadai Tegegn and Tristan Kalloniatis and Omayma Mahjoub and Matthew Macfarlane and Andries P. Smit and Nathan Grinsztajn and Raphael Boige and Cemlyn N. Waters and Mohamed A. Mimouni and Ulrich A. Mbou Sob and Ruan de Kock and Siddarth Singh and Daniel Furelos-Blanco and Victor Le and Arnu Pretorius and Alexandre Laterre},
year={2024},
eprint={2306.09884},
url={https://arxiv.org/abs/2306.09884},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
## See Also π
Other works have embraced the approach of writing RL environments in JAX.
In particular, we suggest users check out the following sister repositories:
- π€ [Qdax](https://github.com/adaptive-intelligent-robotics/QDax) is a library to accelerate
Quality-Diversity and neuro-evolution algorithms through hardware accelerators and parallelization.
- π³ [Evojax](https://github.com/google/evojax) provides tools to enable neuroevolution algorithms
to work with neural networks running across multiple TPU/GPUs.
- π¦Ύ [Brax](https://github.com/google/brax) is a differentiable physics engine that simulates
environments made up of rigid bodies, joints, and actuators.
- ποΈβ [Gymnax](https://github.com/RobertTLange/gymnax) implements classic environments including
classic control, bsuite, MinAtar and a collection of meta RL tasks.
- π² [Pgx](https://github.com/sotetsuk/pgx) provides classic board game environments like
Backgammon, Shogi, and Go.
## Acknowledgements π
The development of this library was supported with Cloud TPUs
from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC) π€.
Raw data
{
"_id": null,
"home_page": null,
"name": "jumanji",
"maintainer": null,
"docs_url": null,
"requires_python": ">=3.10",
"maintainer_email": null,
"keywords": "reinforcement-learning, python, jax",
"author": null,
"author_email": "InstaDeep Ltd <clement.bonnet16@gmail.com>",
"download_url": "https://files.pythonhosted.org/packages/4f/22/5a46dce7904cf51816450338880e59c5c2609fae519af7a4191cdea8b000/jumanji-1.1.0.tar.gz",
"platform": null,
"description": "<p align=\"center\">\n <a href=\"docs/img/jumanji_logo.png\">\n <img src=\"docs/img/jumanji_logo.png\" alt=\"Jumanji logo\" width=\"50%\"/>\n </a>\n</p>\n\n[](https://www.python.org/doc/versions/)\n[](https://badge.fury.io/py/jumanji)\n[](https://github.com/instadeepai/jumanji/actions/workflows/tests_linters.yml)\n[](https://github.com/astral-sh/ruff)\n[](http://mypy-lang.org/)\n[](https://opensource.org/licenses/Apache-2.0)\n[](https://huggingface.co/InstaDeepAI)\n\n[**Environments**](#environments)\n| [**Installation**](#install)\n| [**Quickstart**](#quickstart)\n| [**Training**](#training)\n| [**Citation**](#citing)\n| [**Docs**](https://instadeepai.github.io/jumanji)\n---\n\n<div class=\"collage\">\n <div class=\"row\" align=\"center\">\n <img src=\"docs/env_anim/bin_pack.gif\" alt=\"BinPack\" width=\"16%\">\n <img src=\"docs/env_anim/cleaner.gif\" alt=\"Cleaner\" width=\"16%\">\n <img src=\"docs/env_anim/connector.gif\" alt=\"Connector\" width=\"16%\">\n <img src=\"docs/env_anim/cvrp.gif\" alt=\"CVRP\" width=\"16%\">\n <img src=\"docs/env_anim/flat_pack.gif\" alt=\"FlatPack\" width=\"16%\">\n <img src=\"docs/env_anim/game_2048.gif\" alt=\"Game2048\" width=\"16%\">\n </div>\n <div class=\"row\" align=\"center\">\n <img src=\"docs/env_anim/graph_coloring.gif\" alt=\"GraphColoring\" width=\"16%\">\n <img src=\"docs/env_anim/job_shop.gif\" alt=\"JobShop\" width=\"16%\">\n <img src=\"docs/env_anim/knapsack.gif\" alt=\"Knapsack\" width=\"16%\">\n <img src=\"docs/env_anim/maze.gif\" alt=\"Maze\" width=\"16%\">\n <img src=\"docs/env_anim/minesweeper.gif\" alt=\"Minesweeper\" width=\"16%\">\n <img src=\"docs/env_anim/mmst.gif\" alt=\"MMST\" width=\"16%\">\n </div>\n <div class=\"row\" align=\"center\">\n <img src=\"docs/env_anim/multi_cvrp.gif\" alt=\"MultiCVRP\" width=\"16%\">\n <img src=\"docs/env_anim/pac_man.gif\" alt=\"PacMan\" width=\"12.9%\">\n <img src=\"docs/env_anim/robot_warehouse.gif\" alt=\"RobotWarehouse\" width=\"16%\">\n <img src=\"docs/env_anim/rubiks_cube.gif\" alt=\"RubiksCube\" width=\"16%\">\n <img src=\"docs/env_anim/sliding_tile_puzzle.gif\" alt=\"SlidingTilePuzzle\" width=\"16%\">\n <img src=\"docs/env_anim/snake.gif\" alt=\"Snake\" width=\"16%\">\n </div>\n <div class=\"row\" align=\"center\">\n <img src=\"docs/env_anim/sokoban.gif\" alt=\"RobotWarehouse\" width=\"16%\">\n <img src=\"docs/env_anim/sudoku.gif\" alt=\"Sudoku\" width=\"16%\">\n <img src=\"docs/env_anim/tetris.gif\" alt=\"Tetris\" width=\"16%\">\n <img src=\"docs/env_anim/tsp.gif\" alt=\"Tetris\" width=\"16%\">\n <img src=\"docs/env_anim/lbf.gif\" alt=\"Level-Based Foraging\" width=\"16%\">\n </div>\n</div>\n\n## Jumanji @ ICLR 2024\n\nJumanji has been accepted at [ICLR 2024](https://iclr.cc/), check out our [research paper](https://arxiv.org/abs/2306.09884).\n\n## Welcome to the Jungle! \ud83c\udf34\n\nJumanji is a diverse suite of scalable reinforcement learning environments written in JAX. It now features 22 environments!\n\nJumanji is helping pioneer a new wave of hardware-accelerated research and development in the\nfield of RL. Jumanji's high-speed environments enable faster iteration and large-scale\nexperimentation while simultaneously reducing complexity. Originating in the research team at\n[InstaDeep](https://www.instadeep.com/), Jumanji is now developed jointly with the open-source\ncommunity. To join us in these efforts, reach out, raise issues and read our\n[contribution guidelines](https://github.com/instadeepai/jumanji/blob/main/CONTRIBUTING.md) or just\n[star](https://github.com/instadeepai/jumanji) \ud83c\udf1f to stay up to date with the latest developments!\n\n### Goals \ud83d\ude80\n\n1. Provide a simple, well-tested API for JAX-based environments.\n2. Make research in RL more accessible.\n3. Facilitate the research on RL for problems in the industry and help close the gap between\nresearch and industrial applications.\n4. Provide environments whose difficulty can be scaled to be arbitrarily hard.\n\n### Overview \ud83e\udd9c\n\n- \ud83e\udd51 **Environment API**: core abstractions for JAX-based environments.\n- \ud83d\udd79\ufe0f **Environment Suite**: a collection of RL environments ranging from simple games to NP-hard\ncombinatorial problems.\n- \ud83c\udf6c **Wrappers**: easily connect to your favourite RL frameworks and libraries such as\n[Acme](https://github.com/deepmind/acme),\n[Stable Baselines3](https://github.com/DLR-RM/stable-baselines3),\n[RLlib](https://docs.ray.io/en/latest/rllib/index.html), [Gymnasium](https://github.com/Farama-Foundation/Gymnasium)\nand [DeepMind-Env](https://github.com/deepmind/dm_env) through our `dm_env` and `gym` wrappers.\n- \ud83c\udf93 **Examples**: guides to facilitate Jumanji's adoption and highlight the added value of\nJAX-based environments.\n- \ud83c\udfce\ufe0f **Training:** example agents that can be used as inspiration for the agents one may implement\nin their research.\n\n<h2 name=\"environments\" id=\"environments\">Environments \ud83c\udf0d</h2>\n\nJumanji provides a diverse range of environments ranging from simple games to NP-hard combinatorial\nproblems.\n\n| Environment | Category | Registered Version(s) | Source | Description |\n|------------------------------------------|----------|------------------------------------------------------|--------------------------------------------------------------------------------------------------|------------------------------------------------------------------------|\n| \ud83d\udd22 Game2048 | Logic | `Game2048-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/game_2048/) | [doc](https://instadeepai.github.io/jumanji/environments/game_2048/) |\n| \ud83c\udfa8 GraphColoring | Logic | `GraphColoring-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/graph_coloring/) | [doc](https://instadeepai.github.io/jumanji/environments/graph_coloring/) |\n| \ud83d\udca3 Minesweeper | Logic | `Minesweeper-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/minesweeper/) | [doc](https://instadeepai.github.io/jumanji/environments/minesweeper/) |\n| \ud83c\udfb2 RubiksCube | Logic | `RubiksCube-v0`<br/>`RubiksCube-partly-scrambled-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/rubiks_cube/) | [doc](https://instadeepai.github.io/jumanji/environments/rubiks_cube/) |\n| \ud83d\udd00 SlidingTilePuzzle | Logic | `SlidingTilePuzzle-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/sliding_tile_puzzle/) | [doc](https://instadeepai.github.io/jumanji/environments/sliding_tile_puzzle/) |\n| \u270f\ufe0f Sudoku | Logic | `Sudoku-v0` <br/>`Sudoku-very-easy-v0`| [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/logic/sudoku/) | [doc](https://instadeepai.github.io/jumanji/environments/sudoku/) |\n| \ud83d\udce6 BinPack (3D BinPacking Problem) | Packing | `BinPack-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/bin_pack/) | [doc](https://instadeepai.github.io/jumanji/environments/bin_pack/) |\n| \ud83e\udde9 FlatPack (2D Grid Filling Problem) | Packing | `FlatPack-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/flat_pack/) | [doc](https://instadeepai.github.io/jumanji/environments/flat_pack/) |\n| \ud83c\udfed JobShop (Job Shop Scheduling Problem) | Packing | `JobShop-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/job_shop/) | [doc](https://instadeepai.github.io/jumanji/environments/job_shop/) |\n| \ud83c\udf92 Knapsack | Packing | `Knapsack-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/knapsack/) | [doc](https://instadeepai.github.io/jumanji/environments/knapsack/) |\n| \u2592 Tetris | Packing | `Tetris-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/packing/tetris/) | [doc](https://instadeepai.github.io/jumanji/environments/tetris/) |\n| \ud83e\uddf9 Cleaner | Routing | `Cleaner-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/cleaner/) | [doc](https://instadeepai.github.io/jumanji/environments/cleaner/) |\n| :link: Connector | Routing | `Connector-v2` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/connector/) | [doc](https://instadeepai.github.io/jumanji/environments/connector/) |\n| \ud83d\ude9a CVRP (Capacitated Vehicle Routing Problem) | Routing | `CVRP-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/cvrp/) | [doc](https://instadeepai.github.io/jumanji/environments/cvrp/) |\n| \ud83d\ude9a MultiCVRP (Multi-Agent Capacitated Vehicle Routing Problem) | Routing | `MultiCVRP-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/multi_cvrp/) | [doc](https://instadeepai.github.io/jumanji/environments/multi_cvrp/) |\n| :mag: Maze | Routing | `Maze-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/maze/) | [doc](https://instadeepai.github.io/jumanji/environments/maze/) |\n| :robot: RobotWarehouse | Routing | `RobotWarehouse-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/robot_warehouse/) | [doc](https://instadeepai.github.io/jumanji/environments/robot_warehouse/) |\n| \ud83d\udc0d Snake | Routing | `Snake-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/snake/) | [doc](https://instadeepai.github.io/jumanji/environments/snake/) |\n| \ud83d\udcec TSP (Travelling Salesman Problem) | Routing | `TSP-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/tsp/) | [doc](https://instadeepai.github.io/jumanji/environments/tsp/) |\n| Multi Minimum Spanning Tree Problem | Routing | `MMST-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/mmst) | [doc](https://instadeepai.github.io/jumanji/environments/mmst/) |\n| \u15e7\u2022\u2022\u2022\u15e3\u2022\u2022 PacMan | Routing | `PacMan-v1` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/pac_man/) | [doc](https://instadeepai.github.io/jumanji/environments/pac_man/)\n| \ud83d\udc7e Sokoban | Routing | `Sokoban-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/sokoban/) | [doc](https://instadeepai.github.io/jumanji/environments/sokoban/) |\n| \ud83c\udf4e Level-Based Foraging | Routing | `LevelBasedForaging-v0` | [code](https://github.com/instadeepai/jumanji/tree/main/jumanji/environments/routing/lbf/) | [doc](https://instadeepai.github.io/jumanji/environments/lbf/) |\n\n<h2 name=\"install\" id=\"install\">Installation \ud83c\udfac</h2>\n\nYou can install the latest release of Jumanji from PyPI:\n\n```bash\npip install -U jumanji\n```\n\nAlternatively, you can install the latest development version directly from GitHub:\n\n```bash\npip install git+https://github.com/instadeepai/jumanji.git\n```\n\nJumanji has been tested on Python 3.10, 3.11 and 3.12.\nNote that because the installation of JAX differs depending on your hardware accelerator,\nwe advise users to explicitly install the correct JAX version (see the\n[official installation guide](https://github.com/google/jax#installation)).\n\n**Rendering:** Matplotlib is used for rendering all the environments. To visualize the environments\nyou will need a GUI backend. For example, on Linux, you can install Tk via:\n`apt-get install python3-tk`, or using conda: `conda install tk`. Check out\n[Matplotlib backends](https://matplotlib.org/stable/users/explain/backends.html) for a list of\nbackends you can use.\n\n<h2 name=\"quickstart\" id=\"quickstart\">Quickstart \u26a1</h2>\n\nRL practitioners will find Jumanji's interface familiar as it combines the widely adopted\n[OpenAI Gym](https://github.com/openai/gym) and\n[DeepMind Environment](https://github.com/deepmind/dm_env) interfaces. From OpenAI Gym, we adopted\nthe idea of a `registry` and the `render` method, while our `TimeStep` structure is inspired by\nDeepMind Environment.\n\n### Basic Usage \ud83e\uddd1\u200d\ud83d\udcbb\n\n```python\nimport jax\nimport jumanji\n\n# Instantiate a Jumanji environment using the registry\nenv = jumanji.make('Snake-v1')\n\n# Reset your (jit-able) environment\nkey = jax.random.PRNGKey(0)\nstate, timestep = jax.jit(env.reset)(key)\n\n# (Optional) Render the env state\nenv.render(state)\n\n# Interact with the (jit-able) environment\naction = env.action_spec.generate_value() # Action selection (dummy value here)\nstate, timestep = jax.jit(env.step)(state, action) # Take a step and observe the next state and time step\n```\n\n- `state` represents the internal state of the environment: it contains all the information required\nto take a step when executing an action. This should **not** be confused with the `observation`\ncontained in the `timestep`, which is the information perceived by the agent.\n- `timestep` is a dataclass containing `step_type`, `reward`, `discount`, `observation` and\n`extras`. This structure is similar to\n[`dm_env.TimeStep`](https://github.com/deepmind/dm_env/blob/master/docs/index.md) except for the\n`extras` field that was added to allow users to log environments metrics that are neither part of\nthe agent's observation nor part of the environment's internal state.\n\n### Advanced Usage \ud83e\uddd1\u200d\ud83d\udd2c\n\nBeing written in JAX, Jumanji's environments benefit from many of its features including\nautomatic vectorization/parallelization (`jax.vmap`, `jax.pmap`) and JIT-compilation (`jax.jit`),\nwhich can be composed arbitrarily.\nWe provide an example of a more advanced usage in the\n[advanced usage guide](https://instadeepai.github.io/jumanji/guides/advanced_usage/).\n\n### Registry and Versioning \ud83d\udcd6\n\nLike OpenAI Gym, Jumanji keeps a strict versioning of its environments for reproducibility reasons.\nWe maintain a registry of standard environments with their configuration.\nFor each environment, a version suffix is appended, e.g. `Snake-v1`.\nWhen changes are made to environments that might impact learning results,\nthe version number is incremented by one to prevent potential confusion.\nFor a full list of registered versions of each environment, check out\n[the documentation](https://instadeepai.github.io/jumanji/environments/tsp/).\n\n<h2 name=\"training\" id=\"training\">Training \ud83c\udfce\ufe0f</h2>\n\nTo showcase how to train RL agents on Jumanji environments, we provide a random agent and a vanilla\nactor-critic (A2C) agent. These agents can be found in\n[jumanji/training/](https://github.com/instadeepai/jumanji/tree/main/jumanji/training/).\n\nBecause the environment framework in Jumanji is so flexible, it allows pretty much any problem to\nbe implemented as a Jumanji environment, giving rise to very diverse observations. For this reason,\nenvironment-specific networks are required to capture the symmetries of each environment.\nAlongside the A2C agent implementation, we provide examples of such environment-specific\nactor-critic networks in\n[jumanji/training/networks](https://github.com/instadeepai/jumanji/tree/main/jumanji/training/networks/).\n\n> \u26a0\ufe0f The example agents in `jumanji/training` are **only** meant to serve as inspiration for how one\n> can implement an agent. Jumanji is first and foremost a library of environments - as such, the\n> agents and networks will **not** be maintained to a production standard.\n\nFor more information on how to use the example agents, see the\n[training guide](https://instadeepai.github.io/jumanji/guides/training/).\n\n## Contributing \ud83e\udd1d\n\nContributions are welcome! See our issue tracker for\n[good first issues](https://github.com/instadeepai/jumanji/labels/good%20first%20issue). Please read\nour [contributing guidelines](https://github.com/instadeepai/jumanji/blob/main/CONTRIBUTING.md) for\ndetails on how to submit pull requests, our Contributor License Agreement, and community guidelines.\n\n<h2 name=\"citing\" id=\"citing\">Citing Jumanji \u270f\ufe0f</h2>\n\nIf you use Jumanji in your work, please cite the library using:\n\n```\n@misc{bonnet2024jumanji,\n title={Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX},\n author={Cl\u00e9ment Bonnet and Daniel Luo and Donal Byrne and Shikha Surana and Sasha Abramowitz and Paul Duckworth and Vincent Coyette and Laurence I. Midgley and Elshadai Tegegn and Tristan Kalloniatis and Omayma Mahjoub and Matthew Macfarlane and Andries P. Smit and Nathan Grinsztajn and Raphael Boige and Cemlyn N. Waters and Mohamed A. Mimouni and Ulrich A. Mbou Sob and Ruan de Kock and Siddarth Singh and Daniel Furelos-Blanco and Victor Le and Arnu Pretorius and Alexandre Laterre},\n year={2024},\n eprint={2306.09884},\n url={https://arxiv.org/abs/2306.09884},\n archivePrefix={arXiv},\n primaryClass={cs.LG}\n}\n```\n\n## See Also \ud83d\udd0e\n\nOther works have embraced the approach of writing RL environments in JAX.\nIn particular, we suggest users check out the following sister repositories:\n\n- \ud83e\udd16 [Qdax](https://github.com/adaptive-intelligent-robotics/QDax) is a library to accelerate\nQuality-Diversity and neuro-evolution algorithms through hardware accelerators and parallelization.\n- \ud83c\udf33 [Evojax](https://github.com/google/evojax) provides tools to enable neuroevolution algorithms\nto work with neural networks running across multiple TPU/GPUs.\n- \ud83e\uddbe [Brax](https://github.com/google/brax) is a differentiable physics engine that simulates\nenvironments made up of rigid bodies, joints, and actuators.\n- \ud83c\udfcb\ufe0f\u200d [Gymnax](https://github.com/RobertTLange/gymnax) implements classic environments including\nclassic control, bsuite, MinAtar and a collection of meta RL tasks.\n- \ud83c\udfb2 [Pgx](https://github.com/sotetsuk/pgx) provides classic board game environments like\nBackgammon, Shogi, and Go.\n\n## Acknowledgements \ud83d\ude4f\n\nThe development of this library was supported with Cloud TPUs\nfrom Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC) \ud83c\udf24.\n",
"bugtrack_url": null,
"license": "Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a \"NOTICE\" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License. 5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions. 6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file. 7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License. 8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages. 9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability. END OF TERMS AND CONDITIONS APPENDIX: How to apply the Apache License to your work. To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets \"[]\" replaced with your own identifying information. (Don't include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same \"printed page\" as the copyright notice for easier identification within third-party archives. Copyright 2022 InstaDeep Ltd Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. ",
"summary": "A diverse suite of scalable reinforcement learning environments in JAX",
"version": "1.1.0",
"project_urls": {
"Bug Tracker": "https://github.com/instadeep/jumanji/issues",
"Documentation": "https://instadeepai.github.io/jumanji",
"Homepage": "https://github.com/instadeep/jumanji"
},
"split_keywords": [
"reinforcement-learning",
" python",
" jax"
],
"urls": [
{
"comment_text": "",
"digests": {
"blake2b_256": "6f19ab29f863d2ec070ada665172d36753887f9ca603a7f665dc1e556ff2c7b3",
"md5": "b5da113afa76d094bef01f1b3fa333b4",
"sha256": "9f30a1ac5076df998b2e18f62977f41c641ecd7189fe2b65137fb01c1efb0779"
},
"downloads": -1,
"filename": "jumanji-1.1.0-py3-none-any.whl",
"has_sig": false,
"md5_digest": "b5da113afa76d094bef01f1b3fa333b4",
"packagetype": "bdist_wheel",
"python_version": "py3",
"requires_python": ">=3.10",
"size": 1238589,
"upload_time": "2024-11-22T16:29:35",
"upload_time_iso_8601": "2024-11-22T16:29:35.608538Z",
"url": "https://files.pythonhosted.org/packages/6f/19/ab29f863d2ec070ada665172d36753887f9ca603a7f665dc1e556ff2c7b3/jumanji-1.1.0-py3-none-any.whl",
"yanked": false,
"yanked_reason": null
},
{
"comment_text": "",
"digests": {
"blake2b_256": "4f225a46dce7904cf51816450338880e59c5c2609fae519af7a4191cdea8b000",
"md5": "89da54b66b87c0b2180b5f2ee48dcedc",
"sha256": "1baa7655bb59060f2435531e449baf2c24e7481ee02725bb34c05a722ac44b57"
},
"downloads": -1,
"filename": "jumanji-1.1.0.tar.gz",
"has_sig": false,
"md5_digest": "89da54b66b87c0b2180b5f2ee48dcedc",
"packagetype": "sdist",
"python_version": "source",
"requires_python": ">=3.10",
"size": 1039586,
"upload_time": "2024-11-22T16:29:37",
"upload_time_iso_8601": "2024-11-22T16:29:37.632831Z",
"url": "https://files.pythonhosted.org/packages/4f/22/5a46dce7904cf51816450338880e59c5c2609fae519af7a4191cdea8b000/jumanji-1.1.0.tar.gz",
"yanked": false,
"yanked_reason": null
}
],
"upload_time": "2024-11-22 16:29:37",
"github": true,
"gitlab": false,
"bitbucket": false,
"codeberg": false,
"github_user": "instadeep",
"github_project": "jumanji",
"github_not_found": true,
"lcname": "jumanji"
}
