| Name | rlgym-learn JSON |
| Version |
0.1.4
JSON |
| download |
| home_page | None |
| Summary | Rust backend for the more expensive parts of the rlgym-learn python module |
| upload_time | 2025-02-25 05:04:06 |
| maintainer | None |
| docs_url | None |
| author | None |
| requires_python | <3.13,>=3.8 |
| license | Apache-2.0 |
| keywords |
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| VCS |
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| bugtrack_url |
|
| requirements |
No requirements were recorded.
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| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# RLGym-Learn
A flexible framework for efficiently using [RLGym v2](https://rlgym.org) to train models.
## Features
- Full support for all generics of the RLGym v2 API
- Full support for all functionality of RLGym v2 across multiple environments
- Fast parallelization of environments using Rust and shared memory
- Support for metrics gathering from environments
- Detailed checkpointing system
- File-based configuration
- Provided optimized PPO implementation
- Allows multiple learning algorithms to provide actions for agents within an environment
- Multi-platform (Windows, Linux)
## Installation
1. install RLGym via `pip install rlgym`. If you're here for Rocket League, you can use `pip install rlgym[rl-rlviser]` instead to get the RLGym API as well as the Rocket League / Sim submodules and [rlviser](https://github.com/VirxEC/rlviser) support.
2. If you would like to use a GPU install [PyTorch with CUDA](https://pytorch.org/get-started/locally/)
3. Install Rust by following the instructions [here](https://rustup.rs/)
4. Install this project via `pip install rlgym-learn`
## Usage
See the [RLGym website](https://rlgym.org/RLGym%20Learn/introduction/) for complete documentation and demonstration of functionality [COMING SOON]. For now, you can take a look at `quick_start_guide.py` and `speed_test.py` to get a sense of what's going on.
## Credits
This project was built using Matthew Allen's wonderful [RLGym-PPO](https://github.com/AechPro/rlgym-ppo) as a starting point. Although this project has grown to share almost no code with its predecessor, I couldn't have done this without his support in talking through the design of abstractions and without RLGym-PPO to reference. A couple files in this project remain quite similar or even identical to their counterparts in RLGym-ppo - these include:
- experience_buffer.py
- all of standard_impl/util
- actor / critic implementations in standard_impl/ppo
## Disclaimer
This framework is designed to be usable in every situation you might use the RLGym API in. However, there are a couple assumptions on the usage of RLGym which are baked into the functionality of this framework. These are pretty niche, but are listed below just in case:
1. The AgentID hash must fit into a signed 64 bit integer.
2. The obs space type and action space type should not change after the associated configuration objects' associated get_x_type functions have been called, and they should be the same across all agents and all envs.
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"description": "# RLGym-Learn\nA flexible framework for efficiently using [RLGym v2](https://rlgym.org) to train models.\n\n## Features\n- Full support for all generics of the RLGym v2 API\n- Full support for all functionality of RLGym v2 across multiple environments\n- Fast parallelization of environments using Rust and shared memory\n- Support for metrics gathering from environments\n- Detailed checkpointing system\n- File-based configuration\n- Provided optimized PPO implementation\n- Allows multiple learning algorithms to provide actions for agents within an environment\n- Multi-platform (Windows, Linux)\n\n## Installation\n1. install RLGym via `pip install rlgym`. If you're here for Rocket League, you can use `pip install rlgym[rl-rlviser]` instead to get the RLGym API as well as the Rocket League / Sim submodules and [rlviser](https://github.com/VirxEC/rlviser) support. \n2. If you would like to use a GPU install [PyTorch with CUDA](https://pytorch.org/get-started/locally/)\n3. Install Rust by following the instructions [here](https://rustup.rs/)\n4. Install this project via `pip install rlgym-learn`\n\n## Usage\nSee the [RLGym website](https://rlgym.org/RLGym%20Learn/introduction/) for complete documentation and demonstration of functionality [COMING SOON]. For now, you can take a look at `quick_start_guide.py` and `speed_test.py` to get a sense of what's going on.\n\n\n## Credits\nThis project was built using Matthew Allen's wonderful [RLGym-PPO](https://github.com/AechPro/rlgym-ppo) as a starting point. Although this project has grown to share almost no code with its predecessor, I couldn't have done this without his support in talking through the design of abstractions and without RLGym-PPO to reference. A couple files in this project remain quite similar or even identical to their counterparts in RLGym-ppo - these include:\n- experience_buffer.py\n- all of standard_impl/util\n- actor / critic implementations in standard_impl/ppo\n\n## Disclaimer\nThis framework is designed to be usable in every situation you might use the RLGym API in. However, there are a couple assumptions on the usage of RLGym which are baked into the functionality of this framework. These are pretty niche, but are listed below just in case:\n1. The AgentID hash must fit into a signed 64 bit integer.\n2. The obs space type and action space type should not change after the associated configuration objects' associated get_x_type functions have been called, and they should be the same across all agents and all envs.\n",
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