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docs/source/envs/dmc.md
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# DeepMind Control (DMC)
<!-- TODO: Add Vid -->
These are the Environment Wrappers for selected
[DeepMind Control](https://github.com/google-deepmind/dm_control)
environments in order to use our Motion Primitive gym interface with them.

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docs/source/envs/fancy/index.rst
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.. raw:: html
<div class='center'>
<img src="../../_static/imgs/Box_Pushing.gif" style="margin: 5%; width: 45%;">
<img src="../../_static/imgs/env_gifs/Box_Pushing.gif" style="margin: 5%; width: 45%;">
<p>Box Pushing Task (<a href="./mujoco.html#box-pushing">fancy/BoxPushingDense-v0</a>)</p>
</div>
<br>

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docs/source/envs/fancy/mujoco.md
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### Box Pushing
<div class='center'>
<img src="../../_static/imgs/Box_Pushing.gif" style="margin: 5%; width: 45%;">
<img src="../../_static/imgs/env_gifs/Box_Pushing.gif" style="margin: 5%; width: 45%;">
</div>
The box-pushing task presents an advanced environment for reinforcement learning (RL) systems, utilizing the versatile Franka Emika Panda robotic arm, which boasts seven degrees of freedom (DoFs). The objective of this task is to precisely manipulate a box to a specified goal location and orientation.
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### Table Tennis
<div class='center'>
<img src="../../_static/imgs/Table_Tennis.gif" style="margin: 5%; width: 45%;">
<img src="../../_static/imgs/env_gifs/Table_Tennis.gif" style="margin: 5%; width: 45%;">
</div>
The table tennis task offers a dynamic and interactive environment designed for the development and testing of reinforcement learning (RL) systems. Using a robotic arm equipped with seven degrees of freedom (DoFs), the challenge is to respond to incoming balls and return them accurately to a specified goal location on the opponent's side of the table.
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### Beer Pong
<div class='center'>
<img src="../../_static/imgs/env_gifs/Beer_Pong.gif" style="margin: 5%; width: 45%;">
</div>
<!-- TODO: Vid is ugly and unsuccessful. Replace. -->
The Beer Pong task is based upon a robotic system with seven Degrees of Freedom (DoF), challenging the robot to throw a ball into a cup placed on a large table. The environment's context is established by the cup's location, defined within a range of x-coordinates from -1.42 to 1.42 meters and y-coordinates from -4.05 to -1.25 meters.
The observation space includes the cosine and sine of the robot's joint angles, the angular velocities, and distances of the ball relative to the top and bottom of the cup, along with the cup's position and the current timestep. The action space for the robot is defined by the torques applied to each joint. For episode-based methods, the parameter space is expanded to 15 dimensions, which includes two weights for the basis functions per joint and the duration of the throw, namely the ball release time.
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A successful throw in this task is determined by the ball landing in the cup at the episode's conclusion, showcasing the robot's ability to accurately predict and execute the complex motion required for this popular party game.
<div class='center'>
<img src="../../_static/imgs/Beer_Pong.gif" style="margin: 5%; width: 45%;">
</div>
<!-- TODO: Vid is ugly and unsuccessful. Replace. -->
| Name | Description | Horizon | Action Dimension | Observation Dimension |
| ------------------------------- | ---------------------------------------------------------------------------------------------- | ------- | ---------------- | --------------------- |
| `fancy/BeerPong-v0` | Beer Pong task, based on a custom environment with multiple task variations | 300 | 3 | 29 |

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docs/source/envs/meta.md
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# Metaworld
<div class='center'>
<img src="../_static/imgs/env_gifs/Metaworld.gif" style="margin: 5%; width: 45%;">
<p>Metaworld Dial Turn Task (metaworld/dial-turn-v2)</p>
</div>
<br>
[Metaworld](https://meta-world.github.io/) is an open-source simulated benchmark designed to advance meta-reinforcement learning and multi-task learning, comprising 50 diverse robotic manipulation tasks. The benchmark features a universal tabletop environment equipped with a simulated Sawyer arm and a variety of everyday objects. This shared environment is pivotal for reusing structured learning and efficiently acquiring related tasks.
## Step-Based Environments

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docs/source/envs/open_ai.md
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# Gymnasium
<div class='center'>
<img src="../_static/imgs/env_gifs/Lunar_Lander.gif" style="margin: 5%; width: 50%;">
</div>
<br>
These are the Environment Wrappers for selected [Farama Gymnasium](https://gymnasium.farama.org/) (previously OpenAI Gym) environments to use the Motion Primitive gym interface for them.
## Step-Based Environments