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# mujoco-maze
[![Black](https://img.shields.io/badge/code%20style-black-000.svg)](https://github.com/psf/black)
Some maze environments for reinforcement learning(RL) using [mujoco-py] and
[openai gym][gym].
@ -22,19 +23,26 @@ Thankfully, this project is based on the code from [rllab] and [tensorflow/mode
- PointPush/AntPush
![PointPush](./screenshots/PointPush.png)
![PointPush](./screenshots/AntPush.png)
- PointPush-v0/AntPush-v0 (Distance-based Reward)
- PointPush-v1/AntPush-v1 (Goal-based Reward)
- PointFall/AntFall
![PointFall](./screenshots/PointFall.png)
![PointFall](./screenshots/AntFall.png)
- PointFall-v0/AntFall-v0 (Distance-based Reward)
- PointFall-v1/AntFall-v1 (Goal-based Reward)
- PointBilliard
![PointBilliard](./screenshots/PointBilliard.png)
- PointBilliard-v0 (Distance-based Reward)
- PointBilliard-v1 (Goal-based Reward)
- PointBilliard-v2 (Multiple Goals (0.5 pt or 1.0 pt))
## Warning
This project has some other environments (e.g., billiard, reacher, and
swimmer) but if they are not on README, they are work in progress and
This project has some other environments (e.g., reacher, and swimmer)
but if they are not on README, they are work in progress and
not tested well.
## License

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</asset>
<worldbody>
<light directional="true" cutoff="100" exponent="1" diffuse="1 1 1" specular=".1 .1 .1" pos="0 0 1.3" dir="-0 0 -1.3" />
<geom name='floor' pos='0 0 0' size='40 40 40' type='plane' conaffinity='1' rgba='0.8 0.9 0.8 1' condim='3' />
<geom name="floor" material="MatPlane" pos="0 0 0" size="40 40 40" type="plane" conaffinity="1" rgba="0.8 0.9 0.8 1" condim="3" />
<body name="torso" pos="0 0 0.75">
<geom name="torso_geom" type="sphere" size="0.25" pos="0 0 0" />
<joint name="root" type="free" limited="false" pos="0 0 0" axis="0 0 1" margin="0.01" armature="0" damping="0" />

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