This is one of the assignment for Frontier Research Practice II (Prof. Dong Hao track,Spring 2023) at Peking University - Practice for reinforcement learning and robot simulator.
- Familiar with Isaac Gym and able to read and understand the code of the official example.
- Familiar with Reinforcement Learning (RL) Algorithm.
If you haven't done it yet, please refer to our Tencent documentation to learn the pre-requisite knowledge
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Task 1: Try to run the code and familiar with the detail: (1) How the RL algorithm interacts with the environment; (2) How the file structure is organized.
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Task 2: To try to build your own environment or algorithm, you can start with these entry ways: (Optional) (1) Change the object or agent and retrain RL; (2) Change the environment parameters (e.g. friction, damping) and retrain RL; (3) Create an new environment, set up a new RL task in the environment and try to train it. (Hard)
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Task 3: Submit your notes, or some findings, or a video of the training results, etc to TA through WeChat
The whole assignment will get full marks if you complete task 1 carefully. Of course, we encourage you to complete other tasks carefully and you will gain more.
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The code has been tested on Ubuntu
18.04/20.04
with Python3.7/3.8
. The minimum recommended NVIDIA driver version for Linux is470.74
(dictated by support of IsaacGym). -
We use Anaconda to create virtual environments. To install
Anaconda
, follow instructions here. -
Details regarding installation of IsaacGym can be found here. We highly recommend you to install the Preview
Release 4 version
of IsaacGym, because other versions may have collision detection issues. DO NOT use./create_conda_env_rlgpu.sh
, We highly recommend you to create a conda enviroment first and then usepip install -e .
to install isaacgym only without create a new environment. -
Ensure that Isaac Gym works on your system by running one of the examples from the
python/examples
directory, likejoint_monkey.py
. Please follow troubleshooting steps described in the Isaac GymPreview Release 3/4 install instructions
if you have any trouble running the samples. -
To install
mani_skill_learn
, you can cloneManiSkill-Learn
and then install the package as following commands:
git clone https://github.com/haosulab/ManiSkill-Learn.git
cd ManiSkill-Learn/
pip install -e .
In the root folder, run:
./scripts/open_door.sh