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
PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis

PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis

Ubisoft 76 Dec 30, 2022
Deep Reinforcement Learning with pytorch & visdom

Deep Reinforcement Learning with pytorch & visdom Sample testings of trained agents (DQN on Breakout, A3C on Pong, DoubleDQN on CartPole, continuous A

Jingwei Zhang 783 Jan 04, 2023
This repository contains code to run experiments in the paper "Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers."

Signal Strength and Noise Drive Feature Preference in CNN Image Classifiers This repository contains code to run experiments in the paper "Signal Stre

0 Jan 19, 2022
some academic posters as references. May we have in-person poster session soon!

some academic posters as references. May we have in-person poster session soon!

Bolei Zhou 472 Jan 06, 2023
This repository allows the user to automatically scale a 3D model/mesh/point cloud on Agisoft Metashape

Metashape-Utils This repository allows the user to automatically scale a 3D model/mesh/point cloud on Agisoft Metashape, given a set of 2D coordinates

INSCRIBE 4 Nov 07, 2022
Learning to Prompt for Continual Learning

Learning to Prompt for Continual Learning (L2P) Official Jax Implementation L2P is a novel continual learning technique which learns to dynamically pr

Google Research 207 Jan 06, 2023
PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos

PyKale is a PyTorch library for multimodal learning and transfer learning as well as deep learning and dimensionality reduction on graphs, images, texts, and videos. By adopting a unified pipeline-ba

PyKale 370 Dec 27, 2022
Can we visualize a large scientific data set with a surrogate model? We're building a GAN for the Earth's Mantle Convection data set to see if we can!

EarthGAN - Earth Mantle Surrogate Modeling Can a surrogate model of the Earth’s Mantle Convection data set be built such that it can be readily run in

Tim 0 Dec 09, 2021
A PyTorch toolkit for 2D Human Pose Estimation.

PyTorch-Pose PyTorch-Pose is a PyTorch implementation of the general pipeline for 2D single human pose estimation. The aim is to provide the interface

Wei Yang 1.1k Dec 30, 2022
Source codes of CenterTrack++ in 2021 ICME Workshop on Big Surveillance Data Processing and Analysis

MOT Tracked object bounding box association (CenterTrack++) New association method based on CenterTrack. Two new branches (Tracked Size and IOU) are a

36 Oct 04, 2022
JDet is Object Detection Framework based on Jittor.

JDet is Object Detection Framework based on Jittor.

135 Dec 14, 2022
[NeurIPS 2021] “Improving Contrastive Learning on Imbalanced Data via Open-World Sampling”,

Improving Contrastive Learning on Imbalanced Data via Open-World Sampling Introduction Contrastive learning approaches have achieved great success in

VITA 24 Dec 17, 2022
This repository is an open-source implementation of the ICRA 2021 paper: Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order Pooling.

Locus This repository is an open-source implementation of the ICRA 2021 paper: Locus: LiDAR-based Place Recognition using Spatiotemporal Higher-Order

Robotics and Autonomous Systems Group 96 Dec 15, 2022
[CVPR2021] De-rendering the World's Revolutionary Artefacts

De-rendering the World's Revolutionary Artefacts Project Page | Video | Paper In CVPR 2021 Shangzhe Wu1,4, Ameesh Makadia4, Jiajun Wu2, Noah Snavely4,

49 Nov 06, 2022
OverFeat is a Convolutional Network-based image classifier and feature extractor.

OverFeat OverFeat is a Convolutional Network-based image classifier and feature extractor. OverFeat was trained on the ImageNet dataset and participat

593 Dec 08, 2022
Lightwood is Legos for Machine Learning.

Lightwood is like Legos for Machine Learning. A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glu

MindsDB Inc 312 Jan 08, 2023
Implementation of "With a Little Help from my Temporal Context: Multimodal Egocentric Action Recognition, BMVC, 2021" in PyTorch

Multimodal Temporal Context Network (MTCN) This repository implements the model proposed in the paper: Evangelos Kazakos, Jaesung Huh, Arsha Nagrani,

Evangelos Kazakos 13 Nov 24, 2022
Code release for ICCV 2021 paper "Anticipative Video Transformer"

Anticipative Video Transformer Ranked first in the Action Anticipation task of the CVPR 2021 EPIC-Kitchens Challenge! (entry: AVT-FB-UT) [project page

Facebook Research 123 Dec 13, 2022
BankNote-Net: Open dataset and encoder model for assistive currency recognition

BankNote-Net: Open Dataset for Assistive Currency Recognition Millions of people around the world have low or no vision. Assistive software applicatio

Microsoft 13 Oct 28, 2022
links and status of cool gradio demos

awesome-demos This is a list of some wonderful demos & applications built with Gradio. Here's how to contribute yours! 🖊️ Natural language processing

Gradio 96 Dec 30, 2022