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
GitHub Repository
[CVPR 2022] Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement

Back To Reality: Weak-supervised 3D Object Detection with Shape-guided Label Enhancement Announcement 🔥 We have not tested the code yet. We will fini

Xiuwei Xu 7 Oct 30, 2022
This is the official code for the paper "Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision"

RUAS This is the official code for the paper "Learning with Nested Scene Modeling and Cooperative Architecture Search for Low-Light Vision" A prelimin

Vision & Optimization Group (VOG) 2 May 05, 2022
A large-scale database for graph representation learning

A large-scale database for graph representation learning

Scott Freitas 29 Nov 25, 2022
Public Code for NIPS submission SimiGrad: Fine-Grained Adaptive Batching for Large ScaleTraining using Gradient Similarity Measurement

Public code for NIPS submission "SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement" This repo co

Heyang Qin 0 Oct 13, 2021
Official PyTorch Implementation of HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning (NeurIPS 2021 Spotlight)

[NeurIPS 2021 Spotlight] HELP: Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning [Paper] This is Official PyTorch implementatio

42 Nov 01, 2022
"Exploring Vision Transformers for Fine-grained Classification" at CVPRW FGVC8

FGVC8 Exploring Vision Transformers for Fine-grained Classification paper presented at the CVPR 2021, The Eight Workshop on Fine-Grained Visual Catego

Marcos V. Conde 19 Dec 06, 2022
An Artificial Intelligence trying to drive a car by itself on a user created map

An Artificial Intelligence trying to drive a car by itself on a user created map

Akhil Sahukaru 17 Jan 13, 2022
WarpDrive: Extremely Fast End-to-End Deep Multi-Agent Reinforcement Learning on a GPU

WarpDrive is a flexible, lightweight, and easy-to-use open-source reinforcement learning (RL) framework that implements end-to-end multi-agent RL on a single GPU (Graphics Processing Unit).

Salesforce 334 Jan 06, 2023
IMBENS: class-imbalanced ensemble learning in Python.

IMBENS: class-imbalanced ensemble learning in Python. Links: [Documentation] [Gallery] [PyPI] [Changelog] [Source] [Download] [知乎/Zhihu] [中文README] [a

Zhining Liu 176 Jan 04, 2023
Code for the Paper "Diffusion Models for Handwriting Generation"

Code for the Paper "Diffusion Models for Handwriting Generation"

62 Dec 21, 2022
SNE-RoadSeg in PyTorch, ECCV 2020

SNE-RoadSeg Introduction This is the official PyTorch implementation of SNE-RoadSeg: Incorporating Surface Normal Information into Semantic Segmentati

242 Dec 20, 2022
GLNet for Memory-Efficient Segmentation of Ultra-High Resolution Images

GLNet for Memory-Efficient Segmentation of Ultra-High Resolution Images Collaborative Global-Local Networks for Memory-Efficient Segmentation of Ultra-

VITA 298 Dec 12, 2022
Emotional conditioned music generation using transformer-based model.

This is the official repository of EMOPIA: A Multi-Modal Pop Piano Dataset For Emotion Recognition and Emotion-based Music Generation. The paper has b

hung anna 96 Nov 09, 2022
Learning What and Where to Draw

###Learning What and Where to Draw Scott Reed, Zeynep Akata, Santosh Mohan, Samuel Tenka, Bernt Schiele, Honglak Lee This is the code for our NIPS 201

Scott Ellison Reed 337 Nov 18, 2022
Tracking Progress in Question Answering over Knowledge Graphs

Tracking Progress in Question Answering over Knowledge Graphs Table of contents Question Answering Systems with Descriptions The QA Systems Table cont

Knowledge Graph Question Answering 47 Jan 02, 2023
Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis"

StrengthNet Implementation of "StrengthNet: Deep Learning-based Emotion Strength Assessment for Emotional Speech Synthesis" https://arxiv.org/abs/2110

RuiLiu 65 Dec 20, 2022
K-Means Clustering and Hierarchical Clustering Unsupervised Learning Solution in Python3.

Unsupervised Learning - K-Means Clustering and Hierarchical Clustering - The Heritage Foundation's Economic Freedom Index Analysis 2019 - By David Sal

David Salako 1 Jan 12, 2022
NeWT: Natural World Tasks

NeWT: Natural World Tasks This repository contains resources for working with the NeWT dataset. ❗ At this time the binary tasks are not publicly avail

Visipedia 26 Oct 18, 2022
A Python Library for Graph Outlier Detection (Anomaly Detection)

PyGOD is a Python library for graph outlier detection (anomaly detection). This exciting yet challenging field has many key applications, e.g., detect

PyGOD Team 757 Jan 04, 2023
TensorFlow-LiveLessons - "Deep Learning with TensorFlow" LiveLessons

TensorFlow-LiveLessons Note that the second edition of this video series is now available here. The second edition contains all of the content from th

Deep Learning Study Group 830 Jan 03, 2023