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-compose up -d

Use EKS

Create EKS resource.

cd terraform
terraform init
terraform plan
terraform apply