StarCraft II Minigames

• Rule based game AI for 5 StarCraft II Minigames
  • max score achieved for these agents:
    • CollectMineralShards: 125
    • CollectMineralsAndGas: 5010
    • DefeatRoaches: 91
    • DefeatZerglingsAndBanelings: 220
    • BuildMarines: 132
• Author
  • 林子恒 1801213773
  • lzhbrian@gmail.com

TOC

[TOC]

 

 

Usage

• Files

  final_agent_lzh.py
  tactics.py

• Install

  • Install StarcraftII from battle.net (or link for linux)
  • download minigame map in link, extract and put in to your StarcraftII/Maps/ directory
  • install pysc2, numpy as instructed (pip install pysc2, numpy)
  • put final_agent_lzh.py and tactics.py in your installed site-packages pysc2/agents path
    • for example ~\Anaconda3\Lib\site-packages\pysc2\agents\ (if you are using Anaconda)

• Usage

  # CollectMineralShards
  python -m pysc2.bin.agent --map CollectMineralShards --agent pysc2.agents.final_agent_lzh.CollectMineralShards --use_feature_units
  
  # CollectMineralsAndGas
  python -m pysc2.bin.agent --map CollectMineralsAndGas --agent pysc2.agents.final_agent_lzh.CollectMineralsAndGas --use_feature_units
  
  # DefeatRoaches
  python -m pysc2.bin.agent --map DefeatRoaches --agent pysc2.agents.final_agent_lzh.DefeatRoaches --use_feature_units
  
  # DefeatZerglingsAndBanelings
  python -m pysc2.bin.agent --map DefeatZerglingsAndBanelings --agent pysc2.agents.final_agent_lzh.DefeatZerglingsAndBanelings --use_feature_units
  
  # BuildMarines
  python -m pysc2.bin.agent --map BuildMarines --agent pysc2.agents.final_agent_lzh.BuildMarines --use_feature_units

 

 

Overall Agent Structure

• Overall structure

  • Definition
    • Tactic: one complete movement (e.g. train an SCV by a CommandCenter)
  • We notice that most tactic should contain 3 steps
    • check if this tactic should be used (e.g. check if we have a CommandCenter , and have enough minerals to train an SCV)
    • select the executor of this tactic, click one unit on the screen, e.g. select the CommandCenter)
    • execute the tactic (use the selected CommandCenter to train an SCV)
  • So we build a basic pipeline for every AI (shown in the figure above)
    • select_new_tactic: for each step, we first make a decision if we want to start a new tactic, or we are in the execution of a tactic.
    • select_tactic: if we want to start a new tactic, then we iteratively go through the tactics' criterion (check_available_func()) to select one.
    • choose_executer: After we choose the tactic, we return a select op (select_executer_func()) to select the executor we want to execute this tactic.
    • exec_selected_tactic: If we are in the execution of a tactic (i.e. we have chosen a tactic and select an executer in the previous step()), we return the execution function (exec_func()) to finally execute the tactic.

• How to write a new agent?

  • Overall:

    • We have a base class TacticAgent in final_agent_lzh.py, which is inherited by other agents.
    • If you want to write a new agent, you need to inherit TacticAgent and rewrite the variable self.possible_tactic_list in the setup() function.
    • Items in self.possible_tactic_list are instances from class Tactic in tactics.py.

  • Example:

    import tactics
    tactic_no_op = tactics.Tactic(
        lambda *argv: True, # check_func
        lambda *argv: FUNCTIONS.no_op(), # select_executor_func
        lambda *argv: FUNCTIONS.no_op()  # execute_func
    )
    
    class NewAgent(TacticAgent):
        def setup(self, obs_spec, action_spec):
            super(NewAgent, self).setup(obs_spec, action_spec)
            self.posible_tactic_list = [
                tactic_no_op,
                ... # other tactics
            ]

Strategy

• Strategy for each game: (Here we briefly summarize them. The code in final_agent_lzh.py is extremely clear and self-documented.)
  • CollectMineralShards
    • Let the two marine collect different mineral shards one by one
  • CollectMineralsAndGas
    • build a new CommandCenter
    • if SupplyDepot count < 3 and CommandCenter count >=2 and food_cap == food_used, then build SupplyDepot
    • if there is idle SCV, then let it harvest mineral
    • train SCV
  • DefeatRoaches
    • Defeat the one with the largest y pos
  • DefeatZerglingsAndBanelings
    • send 3 marines to kill Zerglings (be bait/pioneer to let Baneling explode)
    • After all Banelings are gone, send all army to attack all Zerglings
  • BuildMarine
    • if there is idle SCV, then let it harvest mineral
    • if food_cap == food_used, then build supply depot
    • if Barracks count < 7, then build Barracks
    • if Barracks count >= 7, then train marine
    • if SCV count < 20, then train SCV

 

 

Results

For each game, we run 10 times and record the results. We can see that we outperform DeepMind's best baseline in [1] in terms of mean score across 4 games (except DefeatRoaches) and some of our scores are very competitive compared to human players'.

• CollectMineralShards

  	Mean	Std	Max
  StarCraft GrandMaster in [1]	177	N/A	179
  DeepMind Human Player in [1]	133	N/A	142
  DeepMind best baseline in [1]	104	N/A	137
  Ours	113	6	125

• CollectMineralsAndGas

  	Mean	Std	Max
  StarCraft GrandMaster in [1]	7566	N/A	7566
  DeepMind Human Player in [1]	6880	N/A	6952
  DeepMind best baseline in [1]	3978	N/A	4130
  Ours	4802	100	5010

• DefeatRoaches

  	Mean	Std	Max
  StarCraft GrandMaster in [1]	215	N/A	363
  DeepMind Human Player in [1]	41	N/A	81
  DeepMind best baseline in [1]	101	N/A	373
  Ours	25	24	91

• DefeatZerglingsAndBanelings

  	Mean	Std	Max
  StarCraft GrandMaster in [1]	727	N/A	848
  DeepMind Human Player in [1]	729	N/A	757
  DeepMind best baseline in [1]	96	N/A	444
  Ours	108	41	220

• BuildMarine

  	Mean	Std	Max
  StarCraft GrandMaster in [1]	133	N/A	133
  DeepMind Human Player in [1]	138	N/A	142
  DeepMind best baseline in [1]	6	N/A	62
  Ours	127	5	132

Reference

[1] StarCraft II: A New Challenge for Reinforcement Learning. in ArXiv

Useful links

Some of the API usage is learned from the following links

• https://starcraft.fandom.com/wiki/StarCraft_Wiki
• https://chatbotslife.com/building-a-basic-pysc2-agent-b109cde1477c

License

MIT