{"id":30957258,"url":"https://github.com/laboroai/border","last_synced_at":"2025-09-11T13:45:05.648Z","repository":{"id":44601253,"uuid":"320764406","full_name":"laboroai/border","owner":"laboroai","description":"A reinforcement learning library in Rust","archived":false,"fork":false,"pushed_at":"2025-05-17T09:36:30.000Z","size":2492,"stargazers_count":57,"open_issues_count":14,"forks_count":9,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-07-31T03:51:33.136Z","etag":null,"topics":["reinforcement-learning","rust"],"latest_commit_sha":null,"homepage":"","language":"Rust","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"apache-2.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/laboroai.png","metadata":{"files":{"readme":"README.md","changelog":"CHANGELOG.md","contributing":null,"funding":null,"license":"LICENSE-APACHE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2020-12-12T06:26:05.000Z","updated_at":"2025-07-30T07:27:59.000Z","dependencies_parsed_at":"2024-03-17T09:24:42.299Z","dependency_job_id":"edd7192e-5159-4152-a4be-8d295a48f065","html_url":"https://github.com/laboroai/border","commit_stats":null,"previous_names":[],"tags_count":7,"template":false,"template_full_name":null,"purl":"pkg:github/laboroai/border","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/laboroai%2Fborder","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/laboroai%2Fborder/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/laboroai%2Fborder/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/laboroai%2Fborder/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/laboroai","download_url":"https://codeload.github.com/laboroai/border/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/laboroai%2Fborder/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274648319,"owners_count":25324299,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","status":"online","status_checked_at":"2025-09-11T02:00:13.660Z","response_time":74,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["reinforcement-learning","rust"],"created_at":"2025-09-11T13:45:04.458Z","updated_at":"2025-09-11T13:45:05.641Z","avatar_url":"https://github.com/laboroai.png","language":"Rust","funding_links":[],"categories":["Machine Learning"],"sub_categories":[],"readme":"# Border\n\nA reinforcement learning library in Rust.\n\n[![CI](https://github.com/taku-y/border/actions/workflows/ci.yml/badge.svg)](https://github.com/taku-y/border/actions/workflows/ci.yml)\n[![Latest version](https://img.shields.io/crates/v/border.svg)](https://crates.io/crates/border)\n[![Documentation](https://docs.rs/border/badge.svg)](https://docs.rs/border)\n![License](https://img.shields.io/crates/l/border.svg)\n\nBorder consists of the following crates:\n\n* Core and utility\n  * [border-core](https://crates.io/crates/border-core) ([doc](https://docs.rs/border-core/latest/border_core/)) provides basic traits and functions for environments and reinforcement learning (RL) agents.\n  * [border-tensorboard](https://crates.io/crates/border-tensorboard) ([doc](https://docs.rs/border-core/latest/border_tensorboard/)) implements the `TensorboardRecorder` struct for writing records that can be visualized in Tensorboard, based on [tensorboard-rs](https://crates.io/crates/tensorboard-rs).\n  * [border-mlflow-tracking](https://crates.io/crates/border-mlflow-tracking) ([doc](https://docs.rs/border-core/latest/border_mlflow_tracking/)) provides MLflow tracking support for logging metrics during training via REST API.\n  * [border-async-trainer](https://crates.io/crates/border-async-trainer) ([doc](https://docs.rs/border-core/latest/border_async_trainer/)) defines traits and functions for asynchronous training of RL agents using multiple actors. Each actor runs a sampling process in parallel, where an agent interacts with an environment to collect samples for a shared replay buffer.\n  * [border](https://crates.io/crates/border) serves as a collection of examples.\n* Environment\n  * [border-py-gym-env](https://crates.io/crates/border-py-gym-env) ([doc](https://docs.rs/border-core/latest/border_py_gym_env/)) provides a wrapper for [Gymnasium](https://gymnasium.farama.org) environments written in Python.\n  * [border-atari-env](https://crates.io/crates/border-atari-env) ([doc](https://docs.rs/border-core/latest/border_atari_env/)) implements a wrapper for [atari-env](https://crates.io/crates/atari-env), which is part of [gym-rs](https://crates.io/crates/gym-rs).\n  * [border-minari](https://crates.io/crates/border-minari) ([doc](https://docs.rs/border-core/latest/border_minari/)) provides a wrapper for [Minari](https://minari.farama.org).\n* Agent\n  * [border-tch-agent](https://crates.io/crates/border-tch-agent) ([doc](https://docs.rs/border-core/latest/border_tch_agent/)) implements RL agents based on [tch](https://crates.io/crates/tch), including Deep Q Network (DQN), Implicit Quantile Network (IQN), and Soft Actor-Critic (SAC).\n  * [border-candle-agent](https://crates.io/crates/border-candle-agent) ([doc](https://docs.rs/border-core/latest/border_candle_agent/)) implements RL agents based on [candle](https://crates.io/crates/candle-core).\n  * [border-policy-no-backend](https://crates.io/crates/border-policy-no-backend) ([doc](https://docs.rs/border-core/latest/border_policy_no_backend/)) implements policies that are independent of any deep learning backend, such as Torch.\n\n## Status\n\nBorder is experimental and currently under development. The API is unstable.\n\n## Examples\n\nExample scripts are available in the `examples` directory. These have been tested in Docker containers. Some scripts require several days for the training process, as tested on an Ubuntu 22.04 virtual machine.\n\n## Docker\n\nDocker configuration files for development and testing are available in the [dev-border](https://github.com/taku-y/dev-border) repository. These files are used to set up the development environment, supporting both aarch64 (e.g., M2 MacBook Air) and amd64 architectures.\n\n## License\n\nCrates                    | License\n--------------------------|------------------\n`border-core`             | MIT OR Apache-2.0\n`border-tensorboard`      | MIT OR Apache-2.0\n`border-mlflow-tracking`  | MIT OR Apache-2.0\n`border-async-trainer`    | MIT OR Apache-2.0\n`border-py-gym-env`       | MIT OR Apache-2.0\n`border-atari-env`        | GPL-2.0-or-later\n`border-minari`           | MIT OR Apache-2.0\n`border-tch-agent`        | MIT OR Apache-2.0\n`border-candle-agent`     | MIT OR Apache-2.0\n`border-policy-no-backend`| MIT OR Apache-2.0\n`border`                  | GPL-2.0-or-later\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flaboroai%2Fborder","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flaboroai%2Fborder","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flaboroai%2Fborder/lists"}