Attentive Multi Task Deep Reinforcement Learning
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.
Dependencies
- 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
Getting Started
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
Grid Worlds
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.