The code contains an implementation and environments of Attentive Multitask Deep Reinforcement Learning (Bräm et al.). It uses the A3C algorithm based on the universe-starter-agent.

• Python 2.7 or 3.5
• Golang
• six (for py2/3 compatibility)
• TensorFlow 0.12
• tmux (the start script opens up a tmux session with multiple windows)
• htop (shown in one of the tmux windows)
• gym
• libjpeg-turbo (brew install libjpeg-turbo)
• universe
• opencv-python
• numpy
• scipy

conda create --name universe-starter-agent python=3.5
source activate universe-starter-agent

brew install tmux htop cmake golang libjpeg-turbo      # On Linux use sudo apt-get install -y tmux htop cmake golang libjpeg-dev

pip install "gym[atari]"
pip install universe
pip install six
pip install tensorflow==1.5.0
conda install -y -c https://conda.binstar.org/menpo opencv3
conda install -y numpy
conda install -y scipy

pip install -e /path/to/environments/

Add the following to your .bashrc so that you'll have the correct environment when the train.py script spawns new bash shells source activate universe-starter-agent

python train.py --env-id grid-worlds-v1,grid-worlds-v2 --log-dir /tmp/grid-worlds

The command above will train an agent on the grid-worlds-v1 and grid-worlds-v2 tasks.

Once you start the training process, it will create a tmux session with a window for each of all processes started. You can connect to them by typing tmux a in the console. Once in the tmux session, you can see all your windows with ctrl-b w. To switch to window number 0, type: ctrl-b 0. Look up tmux documentation for more commands.

To access TensorBoard to see various monitoring metrics of the agent, open http://localhost:12345/ in a browser.

You can stop the experiment with tmux kill-session command.