From 7bf227dea26bce04960ae463894f4be7a6c3ff14 Mon Sep 17 00:00:00 2001 From: Alessandro Palmas Date: Thu, 22 Feb 2024 23:53:50 -0500 Subject: [PATCH 1/6] Add DIAMBRA to envs to try --- units/en/unitbonus3/envs-to-try.mdx | 39 +++++++++++++++++++++++++++++ 1 file changed, 39 insertions(+) diff --git a/units/en/unitbonus3/envs-to-try.mdx b/units/en/unitbonus3/envs-to-try.mdx index 6bc4ba9..62d3183 100644 --- a/units/en/unitbonus3/envs-to-try.mdx +++ b/units/en/unitbonus3/envs-to-try.mdx @@ -2,6 +2,45 @@ Here we provide a list of interesting environments you can try to train your agents on: +## DIAMBRA Arena + +MineRL + + +DIAMBRA Arena is a software package featuring a collection of high-quality environments for Reinforcement Learning research and experimentation. It provides a standard interface to popular arcade emulated video games, offering a Python API fully compliant with OpenAI Gym/Gymnasium format, that makes its adoption smooth and straightforward. + +It supports all major Operating Systems (Linux, Windows and MacOS) and can be easily installed via Python PIP. It is completely free to use, the user only needs to register on the official website. + +In addition, its [GitHub repository](https://github.com/diambra/) provides a collection of examples covering main use cases of interest that can be run in just a few steps. + +#### Main Features + +All environments are episodic Reinforcement Learning tasks, with discrete actions (gamepad buttons) and observations composed by screen pixels plus additional numerical data (RAM values like characters health bars or characters stage side). + +They all support both single player (1P) as well as two players (2P) mode, making them the perfect resource to explore Standard RL, Competitive Multi-Agent, Competitive Human-Agent, Self-Play, Imitation Learning and Human-in-the-Loop. + +Interfaced games have been selected among the most popular fighting retro-games. While sharing the same fundamental mechanics, they provide different challenges, with specific features such as different type and number of characters, how to perform combos, health bars recharging, etc. + +DIAMBRA Arena is built to maximize compatibility will all major Reinforcement Learning libraries. It natively provides interfaces with the two most import packages: Stable Baselines 3 and Ray RLlib, while Stable Baselines is also available but deprecated. Their usage is illustrated in the [official documentation](https://docs.diambra.ai/) and in the [DIAMBRA Agents repository](https://github.com/diambra/agents). It can easily be interfaced with any other package in a similar way. + +### Competition Platform + +DIAMBRA also provides a competition platform fully integrated with Hugging Face, on which you can submit your trained agents and compete with other coders around the globe in epic video games tournaments! + +It features a public leaderboard where users are ranked by the best score achieved by their agents in our different environments. + +It also offers the possibility to unlock cool achievements depending on the performances of your agent. + +Submitted agents are evaluated and their episodes are streamed on [DIAMBRA Twitch channel](https://www.twitch.tv/diambra_ai). + +#### References + +To start using this environment, check these resources: +- [Official Docs](https://docs.diambra.ai/) +- [Competition Platform](https://diambra.ai) +- [GitHub](https://github.com/diambra/) +- [Discord](https://diambra.ai/discord) + ## MineRL MineRL From f4e21ebc8d785a88d4bba4efa451446e303c16bb Mon Sep 17 00:00:00 2001 From: Alessandro Palmas Date: Fri, 23 Feb 2024 00:10:43 -0500 Subject: [PATCH 2/6] Add some links --- units/en/unitbonus3/envs-to-try.mdx | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/units/en/unitbonus3/envs-to-try.mdx b/units/en/unitbonus3/envs-to-try.mdx index 62d3183..e342bcb 100644 --- a/units/en/unitbonus3/envs-to-try.mdx +++ b/units/en/unitbonus3/envs-to-try.mdx @@ -4,12 +4,12 @@ Here we provide a list of interesting environments you can try to train your age ## DIAMBRA Arena -MineRL +diambraArena DIAMBRA Arena is a software package featuring a collection of high-quality environments for Reinforcement Learning research and experimentation. It provides a standard interface to popular arcade emulated video games, offering a Python API fully compliant with OpenAI Gym/Gymnasium format, that makes its adoption smooth and straightforward. -It supports all major Operating Systems (Linux, Windows and MacOS) and can be easily installed via Python PIP. It is completely free to use, the user only needs to register on the official website. +It supports all major Operating Systems (Linux, Windows and MacOS) and can be easily installed via [Python PIP](https://pypi.org/project/diambra-arena/). It is completely free to use, the user only needs to register on the official website. In addition, its [GitHub repository](https://github.com/diambra/) provides a collection of examples covering main use cases of interest that can be run in just a few steps. @@ -19,9 +19,9 @@ All environments are episodic Reinforcement Learning tasks, with discrete action They all support both single player (1P) as well as two players (2P) mode, making them the perfect resource to explore Standard RL, Competitive Multi-Agent, Competitive Human-Agent, Self-Play, Imitation Learning and Human-in-the-Loop. -Interfaced games have been selected among the most popular fighting retro-games. While sharing the same fundamental mechanics, they provide different challenges, with specific features such as different type and number of characters, how to perform combos, health bars recharging, etc. +[Interfaced games](https://docs.diambra.ai/envs/games/) have been selected among the most popular fighting retro-games. While sharing the same fundamental mechanics, they provide different challenges, with specific features such as different type and number of characters, how to perform combos, health bars recharging, etc. -DIAMBRA Arena is built to maximize compatibility will all major Reinforcement Learning libraries. It natively provides interfaces with the two most import packages: Stable Baselines 3 and Ray RLlib, while Stable Baselines is also available but deprecated. Their usage is illustrated in the [official documentation](https://docs.diambra.ai/) and in the [DIAMBRA Agents repository](https://github.com/diambra/agents). It can easily be interfaced with any other package in a similar way. +DIAMBRA Arena is built to maximize compatibility will all major Reinforcement Learning libraries. It natively provides interfaces with the two most important packages: Stable Baselines 3 and Ray RLlib, while Stable Baselines is also available but deprecated. Their usage is illustrated in the [official documentation](https://docs.diambra.ai/) and in the [DIAMBRA Agents examples repository](https://github.com/diambra/agents). It can easily be interfaced with any other package in a similar way. ### Competition Platform From cd30c90961cfec1252a90500a5c98a71847e90bb Mon Sep 17 00:00:00 2001 From: Alessandro Palmas Date: Fri, 1 Mar 2024 23:28:19 -0500 Subject: [PATCH 3/6] Updated page --- units/en/unitbonus3/envs-to-try.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/units/en/unitbonus3/envs-to-try.mdx b/units/en/unitbonus3/envs-to-try.mdx index e342bcb..3e33ccc 100644 --- a/units/en/unitbonus3/envs-to-try.mdx +++ b/units/en/unitbonus3/envs-to-try.mdx @@ -9,7 +9,7 @@ Here we provide a list of interesting environments you can try to train your age DIAMBRA Arena is a software package featuring a collection of high-quality environments for Reinforcement Learning research and experimentation. It provides a standard interface to popular arcade emulated video games, offering a Python API fully compliant with OpenAI Gym/Gymnasium format, that makes its adoption smooth and straightforward. -It supports all major Operating Systems (Linux, Windows and MacOS) and can be easily installed via [Python PIP](https://pypi.org/project/diambra-arena/). It is completely free to use, the user only needs to register on the official website. +It supports all major Operating Systems (Linux, Windows and MacOS) and can be easily installed via [Python PIP](https://pypi.org/project/diambra-arena/). It is completely free to use, the user only needs to register on the [official website](https://diambra.ai/register/). In addition, its [GitHub repository](https://github.com/diambra/) provides a collection of examples covering main use cases of interest that can be run in just a few steps. From 2db3b14f4a598ac8a133990a430db2c72c7ffd6b Mon Sep 17 00:00:00 2001 From: Alessandro Palmas Date: Fri, 1 Mar 2024 23:32:13 -0500 Subject: [PATCH 4/6] Update diambra arena image --- units/en/unitbonus3/envs-to-try.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/units/en/unitbonus3/envs-to-try.mdx b/units/en/unitbonus3/envs-to-try.mdx index 3e33ccc..2d10c81 100644 --- a/units/en/unitbonus3/envs-to-try.mdx +++ b/units/en/unitbonus3/envs-to-try.mdx @@ -4,7 +4,7 @@ Here we provide a list of interesting environments you can try to train your age ## DIAMBRA Arena -diambraArena +diambraArena DIAMBRA Arena is a software package featuring a collection of high-quality environments for Reinforcement Learning research and experimentation. It provides a standard interface to popular arcade emulated video games, offering a Python API fully compliant with OpenAI Gym/Gymnasium format, that makes its adoption smooth and straightforward. From e8b6db8a326805265bc0ea9daacd4bb55217d8cd Mon Sep 17 00:00:00 2001 From: Alessandro Palmas Date: Sat, 2 Mar 2024 14:58:10 -0500 Subject: [PATCH 5/6] Update units/en/unitbonus3/envs-to-try.mdx Co-authored-by: Thomas Simonini --- units/en/unitbonus3/envs-to-try.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/units/en/unitbonus3/envs-to-try.mdx b/units/en/unitbonus3/envs-to-try.mdx index 2d10c81..ea2ee6d 100644 --- a/units/en/unitbonus3/envs-to-try.mdx +++ b/units/en/unitbonus3/envs-to-try.mdx @@ -21,7 +21,7 @@ They all support both single player (1P) as well as two players (2P) mode, makin [Interfaced games](https://docs.diambra.ai/envs/games/) have been selected among the most popular fighting retro-games. While sharing the same fundamental mechanics, they provide different challenges, with specific features such as different type and number of characters, how to perform combos, health bars recharging, etc. -DIAMBRA Arena is built to maximize compatibility will all major Reinforcement Learning libraries. It natively provides interfaces with the two most important packages: Stable Baselines 3 and Ray RLlib, while Stable Baselines is also available but deprecated. Their usage is illustrated in the [official documentation](https://docs.diambra.ai/) and in the [DIAMBRA Agents examples repository](https://github.com/diambra/agents). It can easily be interfaced with any other package in a similar way. +DIAMBRA Arena is built to maximize compatibility will all major Reinforcement Learning libraries. It natively provides interfaces with the two most important packages: [Stable Baselines 3](https://stable-baselines3.readthedocs.io/en/master/) and [Ray RLlib](https://docs.ray.io/en/latest/rllib/index.html), while Stable Baselines is also available but deprecated. Their usage is illustrated in the [official documentation](https://docs.diambra.ai/) and in the [DIAMBRA Agents examples repository](https://github.com/diambra/agents). It can easily be interfaced with any other package in a similar way. ### Competition Platform From 382c69caa48054fa322196f54847115289dcdedb Mon Sep 17 00:00:00 2001 From: Alessandro Palmas Date: Sat, 2 Mar 2024 14:59:06 -0500 Subject: [PATCH 6/6] Update units/en/unitbonus3/envs-to-try.mdx Co-authored-by: Thomas Simonini --- units/en/unitbonus3/envs-to-try.mdx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/units/en/unitbonus3/envs-to-try.mdx b/units/en/unitbonus3/envs-to-try.mdx index ea2ee6d..ce372e5 100644 --- a/units/en/unitbonus3/envs-to-try.mdx +++ b/units/en/unitbonus3/envs-to-try.mdx @@ -25,7 +25,7 @@ DIAMBRA Arena is built to maximize compatibility will all major Reinforcement Le ### Competition Platform -DIAMBRA also provides a competition platform fully integrated with Hugging Face, on which you can submit your trained agents and compete with other coders around the globe in epic video games tournaments! +DIAMBRA also provides a competition platform fully integrated with the Hugging Face Hub, on which you can submit your trained agents and compete with other coders around the globe in epic video games tournaments! It features a public leaderboard where users are ranked by the best score achieved by their agents in our different environments.