Autonomous RL via MEDAL

Setup

Install MuJoCo if it is not already the case:

Obtain a license on the MuJoCo website.
Download MuJoCo binaries here.
Unzip the downloaded archive into ~/.mujoco/mujoco200 and place your license key file mjkey.txt at ~/.mujoco.
Use the env variables MUJOCO_PY_MJKEY_PATH and MUJOCO_PY_MUJOCO_PATH to specify the MuJoCo license key path and the MuJoCo directory path.
Append the MuJoCo subdirectory bin path into the env variable LD_LIBRARY_PATH.

Install the following libraries for a linux machine (if not already installed):

sudo apt update
sudo apt install libosmesa6-dev libgl1-mesa-glx libglfw3

Setup EARL Benchmark:

# navigate to where you want to setup EARL
git clone https://github.com/architsharma97/earl_benchmark.git
export PYTHONPATH=$PYTHONPATH:/path/to/earl_benchmark

Install dependencies:

conda env create -f conda_env.yml
conda activate arl

Training

Train an episodic RL agent using SAC:

python3 oracle.py

Train an autonomous RL agent using MEDAL:

python3 medal.py

The training scripts use the config in cfgs/<script_name>.yaml by default. For example, medal.py uses cfgs/medal.yaml. To override the default config, you can either change the values in the config or do it directly in the command line as follows:

python3 medal.py env_name=sawyer_door # to run on sawyer door environment
python3 medal.py env_name=sawyer_peg # sawyer peg environment
python3 medal.py env_name=tabletop_manipulation # tabletop manipulation

You can monitor the results via tensorboard:

tensorboard --logdir exp_local

Acknowledgements

The codebase is built on top of the PyTorch implementation of DrQ-v2, original codebase linked here. We thank the authors for an easy codebase to work with!

architsharma97/medal

Autonomous RL via MEDAL

Setup

Training

Acknowledgements