update

README.md

@@ -13,3 +13,5 @@ MetaGym provides abundant environments for benchmarking Reinforcement Learning a
 - [MetaMaze](metagym/metamaze): Meta maze environment for 3D visual navigation (Oct, 2021)
 
 - [Navigator2D](metagym/navigator2d): Simple 2D navigator meta environment (Oct, 2021)
+
+- [MetaLocomotion](metagym/metalocomotion): Locomotion simulator with diverse geometries (June, 2021)

metagym/metalocomotion/README.md

@@ -0,0 +1,68 @@
+# Introduction
+
+MetaLocomotion implement canonical gym bullet locomotion environments, but with diversed geometries. For instance, in meta ants, different tasks differs in the ant's thigh and thin lengths in its four legs.
+
+Currently we support
+#### Meta Ants
+#### Meta Humanoids
+
+# Install
+
+```bash
+pip install metagym[metalocomotion]
+```
+
+#### For local installation, execute following commands:
+
+```bash
+git clone https://github.com/PaddlePaddle/MetaGym
+cd MetaGym
+pip install .[metalocomotion]
+```
+
+# Quick Start
+
+## Import
+
+Import and create the meta maze environment with 
+```python
+import gym
+import metagym.metalocomotion
+
+loco_env = gym.make("meta-humanoids-v0", enable_render=True) # Running meta humanoids
+#loco_env = gym.make("meta-ants-v0", enable_render=True) # Running meta ants
+```
+
+## Sampling Geometries
+
+Use the following code to sample an unique geometry
+```python
+#Sample a task by specifying the configurations
+task = loco_env.sample_task(
+        task_type = "TRAIN" # we provide 256 geometries for training
+        #task_type = "TEST" # we provide 64 geometries for testing
+        #task_type = "OOD" # we provide 64 Out-of-distribution geometries, which is exceptionally harder
+        )
+```
+
+## Running Locomotion
+```python
+#Set the task configuration to the meta environment
+loco_env.set_task(task)
+loco_env.reset()
+
+#Start the task
+done = False
+while not done:
+    action = loco_env.action_space.sample() 
+    observation, reward, done, info = loco_env.step(action)
+    loco_env.render()
+```
+
+# Examples of Different Geometries
+<img src="https://github.com/benchmarking-rl/PARL-experiments/blob/master/MetaGym/ant_exp.png" width="600"/>
+<img src="https://github.com/benchmarking-rl/PARL-experiments/blob/master/MetaGym/ants_1.gif" width="600"/>
+<img src="https://github.com/benchmarking-rl/PARL-experiments/blob/master/MetaGym/ants_2.gif" width="600"/>
+<img src="https://github.com/benchmarking-rl/PARL-experiments/blob/master/MetaGym/humanoid_exp.png" width="600"/>
+<img src="https://github.com/benchmarking-rl/PARL-experiments/blob/master/MetaGym/humanoids_1.gif" width="600"/>
+<img src="https://github.com/benchmarking-rl/PARL-experiments/blob/master/MetaGym/humanoids_2.gif" width="600"/>

setup.py

@@ -49,6 +49,7 @@
         'six>=1.12.0',
     ],
     extras_require={
+        'metalocomotion': ['numpy>=1.20.3', 'pybullet>=3.0.7'],
         'metamaze': ['pygame>=2.0.2dev2', 'numba>=0.54.0'],
         'quadrupedal': ['scipy>=0.12.0', 'pybullet>=3.0.7', 'attrs>=20.3.0'],
         'quadrotor': ['scipy>=0.12.0', 'networkx>=2.2', 'trimesh>=3.2.39', 'networkx>=2.2', 'colour>=0.1.5'],