Attentive Multi-Tasking

This repository contains the code for my bachelor thesis in multi-task reinforcement learning. It incorporates the idea from: AMT into IMPALA to increase the sample efficiency training.

Extensions:

• IMPALA with PopArt has been implemented.
• An additional self-attention mechanism has been adopted to each subnetwork.

Running the agent

Dependencies

• TensorFlow 1.13.0
• DeepMind Sonnet.
• Atari

There is a Dockerfile which serves as a reference for the pre-requisites and commands needed to run the code.

Local single machine training on multiple atari games.

python atari_experiment.py --num_actors=10 --batch_size=5 \
    --entropy_cost=0.01 --learning_rate=0.0006 \
    --total_environment_frames=2000000000

Run the agent in a distributed setting

Use a multiplexer to execute the following commands.

Learner (for Atari)

python atari_experiment.py --job_name=learner --task=0 --num_actors=30 \
    --level_name=BreakoutNoFrameSkip-v4 --batch_size=32 --entropy_cost=0.01 \
    --learning_rate=0.0006 \
    --total_environment_frames=2000000000 

Actor(s)

for i in $(seq 0 29); do
  python atari_experiment.py --job_name=actor --task=$i \
      --num_actors=30 &
done;
wait

Test Score

Test it across 10 episodes using:

python atari_experiment.py --mode=test --level_name=BreakoutNoFrameSkip-v4 \
    --test_num_episodes=10