Basketball Reinforcement Learning, Timothy Yong.

sudo apt-get install cmake
sudo apt-get install gazebo9
sudo apt-get install libsdformat6-dev
sudo apt-get install libboost-all-dev
sudo apt-get install libarmadillo-dev
sudo apt-get install libprotobuf-dev
sudo apt-get install python3-pip
sudo pip3 install numpy pygame
sudo pip3 install mxnet

Note that installing gazebo/sdf have seen issues in the past. While this particular project uses gazebo9, it should work fine with gazebo8. To use with other versions of sdf, change the sdf format from (1.6) to (1.x), the version of sdf you wish to use, in several of the files (eg. basketball_world.world and ball_gz.cc). For any of the above dependencies which do not exist, please try to install them from source.

In order to run this project, first compile the necessary libraries:

mkdir build && cd build
cmake ..
make

To run the standard DQN (and to save the model after training), just type the following:

cd python
./dqn --save_params newmodel.param --num_rollouts 12800

To view all the flags which you can set, type:

./dqn --help

Now you can run the project. First start by running the gazebo simulator used for visualization:

./scripts/start_armviz.sh

Open a new terminal and navigate to the project directory. To run the DQN model:

cd python
./dqn --render

If you want to stop visualization, run the following:

./killserver.sh

To run PoWER (and to save the model after training), just type the following:

cd python
./power --save_params newmodel.param --num_rollouts 1000