distributed_rl's Introduction

distributed_rl

This is pytorch implementation of distributed deep reinforcement learning.

ape-x
- Distributed Prioritized Experience Replay
r2d2 (Recurrent Replay Distributed DQN)(experimental)
- Recurrent Experience Replay in Distributed Reinforcement Learning

System

In our system, there are two processes, Actor and Learner. In Learner process, thread of the replay memory runs at the same time, and these processes communicate using Redis.

Install

git clone https://github.com/neka-nat/distributed_rl.git
cd distributed_rl
poetry install

Install redis-server.

sudo apt-get install redis-server

Setting Atari. https://github.com/openai/atari-py#roms

Run

The following command is running all actors and learner in localhost. The number of actor's processes is given as an argument.

poetry shell
./run.sh 4

Run r2d2 mode.

./run.sh 4 config/all_r2d2.conf

Docker build

cd distributed_rl
docker build -t distributed_rl:1.0 .

Use AWS

Create AMI.

packer build packer/ubuntu.json

Create key-pair.

aws ec2 create-key-pair --key-name key --query 'KeyMaterial' --output text > ~/.ssh/key.pem
chmod 400 ~/.ssh/key.pem

Run instances.

cd aws
python aws_run_instances.py aws_config.yaml

Run fabric for a learner.

fab -H <Public IP of learner's instance> -u ubuntu -i ~/.ssh/key.pem learner_run

Run fabric for actors.

fab -H <Public IP of actor's instance 1>,<Public IP of actor's instance 2>, ... -u ubuntu -i ~/.ssh/key.pem actor_run:num_proc=15,leaner_host=<Public IP of learner's instance>

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