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".
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 |
- 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.
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.