Awesome

rbi

Implementation of distributed RL algorithms:

Baselines:

1. Ape-X (https://arxiv.org/abs/1803.00933)
2. R2D2 (https://openreview.net/pdf?id=r1lyTjAqYX)
3. PPO (https://arxiv.org/abs/1707.06347)

RBI: A safe reinforcement learning algorithm

Currently, supported environment is ALE

How to run

A distributed RL agent is composed of a single learning process and multiple actor process. Therefore, we need to execute two bash scripts one for the learner and one for the multiple actors.

choose <algorithm> as one of rbi|ape|ppo|r2d2|rbi_rnn

Run Learner:

sh learner.sh <algorithm> <identifier> <game> <new|resume>

resume is a number of experiment to resume. For example:

sh learner.sh rbi qbert_debug qbert new

starts a new experiment, while:

sh learner.sh rbi qbert_debug qbert 3

resumes experiment 3 with identifier qbert_debug

Run Actors:

sh actors.sh <algorithm> <identifier> <game> <resume>

Run Evaluation player:

right now there are two evaluation players in each actors script

Terminate a live run:

1. ctrl-c from the learner process terminal
2. pkill -f "main.py" (kills all the live actor processes)
3. rm -r /dev/shm/<your name>/rbi/* (clear the ramdisk filesystem)

Setup prerequisites before running the code

To login:

ssh <username>@<server-address>

Use ssh-keygen and ssh-copy-id to avoid passwords:

ssh-keygen
ssh-copy-id -i ~/.ssh/id_rsa user@host

Install Anaconda:

copy anaconda file to server and run: sh Anaconda3-2018.12-Linux-x86_64.sh

Install Tmux:

make new directory called tmux_tmp copy ncurses.tar.gz and tmx-2.5.tar.gz to tmux_tmp directory copy install_tmux.sh to server and run ./install_tmux.sh

setup directories, clone rbi and setup conda environment

mkdir -p ~/data/rbi/results
mkdir -p ~/data/rbi/logs
mkdir -p ~/projects
cd ~/projects
git clone https://github.com/eladsar/rbi.git
cd ~/projects/rbi
conda env create -f install/environment.yml
source activate torch1
pip install atari-py

Docker

We also provide a docker file and instructions to build and run the simulation in a docker container.

Please first, install nvidia-docker:

https://github.com/NVIDIA/nvidia-docker

To build the docker container:

cd install
docker image build --tag local:rbi .

To run the docker container:

nvidia-docker run --rm -it --net=host --ipc=host --name rbi1 local:rbi bash

Evaluation

There are three ways to evaluate the learning progress and agent performance

Tensorboard

Each run logs several evaluation metrics such as: (1) loss function (2) network weights (3) score statistics (mean, std, min, max)

To view the tensorboar run an ssh port-forwarding command

ssh -L <port>:127.0.0.1:<port> <server>

and from the server terminal run

cd <outputdir>/results
tensorboard --logdir:<name>:<run directory> --port<port>

Jupyter Notebook

To view a live agent run the evaluate.ipynb notebook. Use the identifier name and the resume parameter to choose the required run. You may also need to change the basedir parameter.

Pandas Dataframe

Performance logs are stored to numpy files and in the end of the run a postprocessing process stores all logs into a pandas dataframe. These dataframes may be used to plot the performance graph with the plot_results.py script.