Thought-SC2

Introduction

This is the code for Thought-Game and the model experiments (implement the WorldModel) on it.

Corresponding to the paper "Efficient Reinforcement Learning for StarCraft by Abstract Forward Models and Transfer Learning"

Also contains a script to train an agent against level-10 in "train_level10_eval_mini_srcgame_add_map_bn".

Contents

The below table shows the directories for the contents of the project. We provide codes for TG-SC2, TG-Zerg, TG-Terran, TG-SC1, TG-Hypothesis, and WorldModel-SC2.

Directory	Content
All except below	Codes for TG-SC2 and WorldModel-SC2
TG-Hypothesis	Codes for different fidelity levels of TG
TG-SC1	Codes for running on SC1 with TorchCraft
TG-Zerg and TG-Terran	Codes for Zerg race and Terran race

Requirements

• python==3.5
• tensorflow==1.5
• future==0.16
• pysc2==1.2
• matplotlib==2.1
• scipy==1.0

Notes:

If you install pysc2==1.2 and find this error "futures requires Python '>=2.6, <3' but the running Python is 3.5.6", then try first install futures as follow

pip install futures==3.1.1

then install pysc2==1.2, and this problem is solved.

Usage

Run eval_mini_srcgame.py to train an agent (P vs. T) in StarCraft II. See eval_mini_srcgame.py for more parameters.

Run testing

python eval_mini_srcgame.py --on_server=False 

Important Parameters

--training:         Whether to train an agent.
--restore_model:    Whether to restore old model.
--on_server:        If want to train on a server in distributed setting, set it to ture.
--map:              Name of a map to use. Default is Simple64.
--agent_race:       Agent's race. Default is P.
--bot_race:         Bot's race. Default is T.
--difficulty:       Bot's strength. Default is 7.
--port_num:         Port number for running SC2.
--max_iters:        How many iterations for training.
--step_mul:         Game speed. Set to 1 while testing. Set to 8 while training.