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
Hunt down social media accounts by username across social networks

Hunt down social media accounts by username across social networks Installation | Usage | Docker Notes | Contributing Installation # clone the repo $

1 Dec 14, 2021
Source code for CVPR2022 paper "Abandoning the Bayer-Filter to See in the Dark"

Abandoning the Bayer-Filter to See in the Dark (CVPR 2022) Paper: https://arxiv.org/abs/2203.04042 (Arxiv version) This code includes the training and

74 Dec 15, 2022
docTR by Mindee (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.

docTR by Mindee (Document Text Recognition) - a seamless, high-performing & accessible library for OCR-related tasks powered by Deep Learning.

Mindee 1.5k Jan 01, 2023
Code for paper: Towards Tokenized Human Dynamics Representation

Video Tokneization Codebase for video tokenization, based on our paper Towards Tokenized Human Dynamics Representation. Prerequisites (tested under Py

Kenneth Li 20 May 31, 2022
[NeurIPS 2021] Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data

Near-Duplicate Video Retrieval with Deep Metric Learning This repository contains the Tensorflow implementation of the paper Near-Duplicate Video Retr

Liming Jiang 238 Nov 25, 2022
mmfewshot is an open source few shot learning toolbox based on PyTorch

OpenMMLab FewShot Learning Toolbox and Benchmark

OpenMMLab 514 Dec 28, 2022
Deep Q-Learning Network in pytorch (not actively maintained)

pytoch-dqn This project is pytorch implementation of Human-level control through deep reinforcement learning and I also plan to implement the followin

Hung-Tu Chen 342 Jan 01, 2023
This repo holds the code of TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation

TransFuse This repo holds the code of TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation Requirements Pytorch=1.6.0, 1.9.0 (=1.

Rayicer 93 Dec 19, 2022
LinkNet - This repository contains our Torch7 implementation of the network developed by us at e-Lab.

LinkNet This repository contains our Torch7 implementation of the network developed by us at e-Lab. You can go to our blogpost or read the article Lin

e-Lab 158 Nov 11, 2022
A library for optimization on Riemannian manifolds

TensorFlow RiemOpt A library for manifold-constrained optimization in TensorFlow. Installation To install the latest development version from GitHub:

Oleg Smirnov 83 Dec 27, 2022
Implementation of Kronecker Attention in Pytorch

Kronecker Attention Pytorch Implementation of Kronecker Attention in Pytorch. Results look less than stellar, but if someone found some context where

Phil Wang 16 May 06, 2022
Swapping face using Face Mesh with TensorFlow Lite

Swapping face using Face Mesh with TensorFlow Lite

iwatake 17 Apr 26, 2022
Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data recorded in NumPy array

shindo.py Calculates JMA (Japan Meteorological Agency) seismic intensity (shindo) scale from acceleration data stored in NumPy array Introduction Japa

RR_Inyo 3 Sep 23, 2022
wmctrl ported to Python Ctypes

work in progress wmctrl is a command that can be used to interact with an X Window manager that is compatible with the EWMH/NetWM specification. wmctr

Iyad Ahmed 22 Dec 31, 2022
StyleGAN - Official TensorFlow Implementation

StyleGAN — Official TensorFlow Implementation Picture: These people are not real – they were produced by our generator that allows control over differ

NVIDIA Research Projects 13.1k Jan 09, 2023
Voice control for Garry's Mod

WIP: Talonvoice GMod integrations Very work in progress voice control demo for Garry's Mod. HOWTO Install https://talonvoice.com/ Press https://i.imgu

Meta Construct 5 Nov 15, 2022
Bling's Object detection tool

BriVL for Building Applications This repo is used for illustrating how to build applications by using BriVL model. This repo is re-implemented from fo

chuhaojin 47 Nov 01, 2022
The Self-Supervised Learner can be used to train a classifier with fewer labeled examples needed using self-supervised learning.

Published by SpaceML • About SpaceML • Quick Colab Example Self-Supervised Learner The Self-Supervised Learner can be used to train a classifier with

SpaceML 92 Nov 30, 2022
K-FACE Analysis Project on Pytorch

Installation Setup with Conda # create a new environment conda create --name insightKface python=3.7 # or over conda activate insightKface #install t

Jung Jun Uk 7 Nov 10, 2022