Deep Reinforcement Learning for Multiplayer Online Battle Arena

Related tags

Deep LearningMOBA_RL
Overview

MOBA_RL

Deep Reinforcement Learning for Multiplayer Online Battle Arena

Prerequisite

  1. Python 3
  2. gym-derk
  3. Tensorflow 2.4.1
  4. Dotaservice of TimZaman
  5. Seed RL of Google
  6. Ubuntu 20.04
  7. RTX 3060 GPU, 16GB RAM is used to run Dota2 environment with rendering
  8. RTX 3080 GPU, 46GB RAM is used to training 16 number of headless Dota2 environment together in my case

Derk Environment

We are going to train small MOBA environment called Derk.

First, move to dr-derks-mutant-battlegrounds folder.

Run below command to run the 50 parallel environemnt. I modified Seel_RL of Google for my MOBA case.

$ python learner_1.py --workspace_path [your path]/dr-derks-mutant-battlegrounds/
$ python learner_2.py --workspace_path [your path]/dr-derks-mutant-battlegrounds/
$ python run.py -p1 bot -p2 oldbot -n 50

You can check the training progress using Tensorboard log under tboard path of workspace.

Dota2 Environment

Rendering Environment

You first need to install Dota 2 from Steam. After installation, please check there is Dota2 folder under /home/[your account]/.steam/steam/steamapps/common/dota 2 beta'. We are going to run Dota2 from terminal command.

Next, you need to download and install dotaservice. In my case, I should modity the _run_dota function of dotaservice.py like below.

async def _run_dota(self):
  script_path = os.path.join(self.dota_path, self.DOTA_SCRIPT_FILENAME)
  script_path = '/home/kimbring2/.local/share/Steam/ubuntu12_32/steam-runtime/run.sh'

  # TODO(tzaman): all these options should be put in a proto and parsed with gRPC Config.
  args = [
       script_path,
       '/home/kimbring2/.local/share/Steam/steamapps/common/dota 2 beta/game/dota.sh',
       '-botworldstatesocket_threaded',
       '-botworldstatetosocket_frames', '{}'.format(self.ticks_per_observation),
       '-botworldstatetosocket_radiant', '{}'.format(self.PORT_WORLDSTATES[TEAM_RADIANT]),
       '-botworldstatetosocket_dire', '{}'.format(self.PORT_WORLDSTATES[TEAM_DIRE]),
       '-con_logfile', 'scripts/vscripts/bots/{}'.format(self.CONSOLE_LOG_FILENAME),
       '-con_timestamp',
       '-console',
       '-dev',
       '-insecure',
       '-noip',
       '-nowatchdog',  # WatchDog will quit the game if e.g. the lua api takes a few seconds.
       '+clientport', '27006',  # Relates to steam client.
       '+dota_1v1_skip_strategy', '1',
       '+dota_surrender_on_disconnect', '0',
       '+host_timescale', '{}'.format(self.host_timescale),
       '+hostname dotaservice',
       '+sv_cheats', '1',
       '+sv_hibernate_when_empty', '0',
       '+tv_delay', '0',
       '+tv_enable', '1',
       '+tv_title', '{}'.format(self.game_id),
       '+tv_autorecord', '1',
       '+tv_transmitall', '1',  # TODO(tzaman): what does this do exactly?
  ]

Training Environment

You need to build the Docker image of Dotaservice mentioned in README of Docker of the dotaservice.

You can run the Seel RL for Dota2 using below command.

$ ./run_dotaservice.sh 16
$ ./run_impala.sh 16

Addidinally, you can terminate all process using below command.

$ ./stop.sh
Owner
Dohyeong Kim
Researchers interested in creating agents that behave like humans using Deep Learning
Dohyeong Kim
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