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https://github.com/scitator/learning-to-move-starter-kit
https://github.com/scitator/learning-to-move-starter-kit
Last synced: 21 days ago
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- Host: GitHub
- URL: https://github.com/scitator/learning-to-move-starter-kit
- Owner: Scitator
- License: mit
- Created: 2019-08-07T11:14:27.000Z (over 5 years ago)
- Default Branch: master
- Last Pushed: 2019-08-07T11:49:18.000Z (over 5 years ago)
- Last Synced: 2024-10-04T17:49:26.399Z (about 1 month ago)
- Language: Python
- Size: 163 KB
- Stars: 9
- Watchers: 2
- Forks: 2
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# [NeurIPS 2019: Learn to Move - Walk Around](https://www.aicrowd.com/challenges/neurips-2019-learn-to-move-walk-around)
[Catalyst](https://github.com/catalyst-team/catalyst) starter kit
## Environment setup.
(Taken from the official [repo](https://github.com/stanfordnmbl/osim-rl)).
**Anaconda** is required to run our simulations. Anaconda will create a virtual environment with all the necessary libraries, to avoid conflicts with libraries in your operating system. You can get anaconda from here https://docs.anaconda.com/anaconda/install/. In the following instructions we assume that Anaconda is successfully installed.
For the challenge we prepared [OpenSim](http://opensim.stanford.edu/) binaries as a conda environment to make the installation straightforward
We support Windows, Linux, and Mac OSX (all in 64-bit). To install our simulator, you first need to create a conda environment with the OpenSim package.
On **Windows**, open a command prompt and type:
```bash
conda create -n opensim-rl -c kidzik opensim python=3.6.1
activate opensim-rl
```
On **Linux/OSX**, run:
```bash
conda create -n opensim-rl -c kidzik opensim python=3.6.1
source activate opensim-rl
```
These commands will create a virtual environment on your computer with the necessary simulation libraries installed. Next, you need to install our python reinforcement learning environment. Type (on all platforms):
```bash
conda install -c conda-forge lapack git
pip install osim-rl
```
### Basic usage
To execute 200 iterations of the simulation enter the `python` interpreter and run the following:
```python
from osim.env import L2M2019Env
env = L2M2019Env(visualize=True)
observation = env.reset()
for i in range(200):
observation, reward, done, info = env.step(env.action_space.sample())
```
## Catalyst.RL
To train agents on the L2M environment, we can run Catalyst.RL as usual.
```bash
export LOGDIR=...
bash bin/prepare_configs.sh
# start db node
mongod --config ./configs/_mongod.conf --port 12000
# start trainer node
export GPUS="" # like GPUS="0" or GPUS="0,1" for multi-gpu training
CUDA_VISIBLE_DEVICES="$GPUS" catalyst-rl run-trainer --config ./configs/_exp_common.yml ./configs/_dpg_common.yml ./configs/ddpg.yml
# start sampler node
CUDA_VISIBLE_DEVICES="" catalyst-rl run-samplers --config ./configs/_exp_common.yml ./configs/_dpg_common.yml ./configs/ddpg.yml
# view tensorboard logs
CUDA_VISIBLE_DEVICE="" tensorboard --logdir=./logs
```
For more information about Catalyst.RL you can check [official repo](https://github.com/catalyst-team/catalyst), [documentaiton](https://catalyst-team.github.io/catalyst/) and [examples](https://github.com/catalyst-team/catalyst/tree/master/examples/rl_gym).
## Results
After a while you can see something like
This shows us mean validation reward based on 64 episodes.
I hope this agent is only the beginning of you RL path and you can do much better.
Enjoy the competition!