MPL is a collection of environments/tasks using MPL system from APL, simulated with the Mujoco physics engine and wrapped in the OpenAI gym API.

MPL uses git submodules to resolve dependencies. Please follow steps exactly as below to install correctly.

0. Ensure you have access these two repositories - MPL and MPL_sim.

1. Clone this repo with pre-populated submodule dependencies

$ git clone --recursive https://github.com/vikashplus/MPL.git

2. Update submodules

$ cd MPL  
$ git submodule update --remote

3. Add repo to pythonpath by updating ~/.bashrc or ~/.bash_profile

export PYTHONPATH="<path/to/MPL>:$PYTHONPATH"

4. Follow install instructions from mjrl to get model free agents for `MPL'
5. To visualize an env using a random policy

python MPL_agents/mjrl/examine_policy.py -e SallyReachRandom-v0

5. To visualize an trained policy

python MPL_agents/mjrl/examine_policy.py -e SallyReachRandom-v0 -p MPL_agents/mjrl/sallyReachRandom-v0/best_policy.pickle