Two OpenAI gym environments with continuous and discrete actions spaces can be found under jackal_navi_envs. They are registered as jackal_discrete-v0 and jackal_continuous-v0. To use the environment locally, you need a workspace with all the jackal dependencies installed. Assume you already set up the Jackal simulation, one extra package jackal_helper need to be installed.
- Under the parent folder of your workspace:
cd src
git clone https://github.com/Daffan/jackal_helper.git
cd ..
source devel/setup.bash
catkin_make
- The following python dependency need to be installed:
pip3 install gym defusedxml pyyaml pyquaternion rospkg
- To test the installation:
source <your_workspace>/devel/setup/bash
python3 scripts/test_env.py
Above command will run a Jackal_navigation_env 10 episodes.
- build the singularity image
sudo singularity build --notest APPLR_melodic.simg APPLR_melodic.def
- Run test in container
./singularity_run.sh python3 scripts/test_env.py
- Check all the environment and training related configuration:
cat continuous/config/td3_condor.json
- Run the central learning node locally at host
./executable/run_central_node.sh
Running the central node locally is currently recommanded. A crowded cluster will usually idle your jobs. When you jobs is idled and recovered, it will initialize a new policy! Run it locally can prevent the issue.
- Run all the actors nodes:
pytho3n gen_sub1.py --num_env <num_env in your config file>
- Run test
python3 scripts/test_policy_condor.py --model <path/to/logfolder> --policy <policy_file_name> --test
- Get the test report
python3 scripts/report_test.py
This will print the averaging of some metrics and generate report.json with all test results.