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
Reference models and tools for Cloud TPUs.

Cloud TPUs This repository is a collection of reference models and tools used with Cloud TPUs. The fastest way to get started training a model on a Cl

5k Jan 05, 2023
An implementation of paper `Real-time Convolutional Neural Networks for Emotion and Gender Classification` with PaddlePaddle.

简介 通过PaddlePaddle框架复现了论文 Real-time Convolutional Neural Networks for Emotion and Gender Classification 中提出的两个模型，分别是SimpleCNN和MiniXception。利用 imdb_crop

8 Mar 11, 2022
Cortex-compatible model server for Python and TensorFlow

Nucleus model server Nucleus is a model server for TensorFlow and generic Python models. It is compatible with Cortex clusters, Kubernetes clusters, a

Cortex Labs 14 Nov 27, 2022
CKD - Collaborative Knowledge Distillation for Heterogeneous Information Network Embedding

Collaborative Knowledge Distillation for Heterogeneous Information Network Embed

zhousheng 9 Dec 05, 2022
A scikit-learn-compatible module for estimating prediction intervals.

|Anaconda|_ MAPIE - Model Agnostic Prediction Interval Estimator MAPIE allows you to easily estimate prediction intervals using your favourite sklearn

SimAI 584 Dec 27, 2022
This is the code for our paper "Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text"

Iconary This is the code for our paper "Iconary: A Pictionary-Based Game for Testing Multimodal Communication with Drawings and Text". It includes the

AI2 6 May 24, 2022
This is an official pytorch implementation of Fast Fourier Convolution.

Fast Fourier Convolution (FFC) for Image Classification This is the official code of Fast Fourier Convolution for image classification on ImageNet. Ma

pkumi 199 Jan 03, 2023
Chainer Implementation of Fully Convolutional Networks. (Training code to reproduce the original result is available.)

fcn - Fully Convolutional Networks Chainer implementation of Fully Convolutional Networks. Installation pip install fcn Inference Inference is done as

Kentaro Wada 218 Oct 27, 2022
LightLog is an open source deep learning based lightweight log analysis tool for log anomaly detection.

LightLog Introduction LightLog is an open source deep learning based lightweight log analysis tool for log anomaly detection. Function description [BG

25 Dec 17, 2022
[SDM 2022] Towards Similarity-Aware Time-Series Classification

SimTSC This is the PyTorch implementation of SDM2022 paper Towards Similarity-Aware Time-Series Classification. We propose Similarity-Aware Time-Serie

Daochen Zha 49 Dec 27, 2022
Novel and high-performance medical image classification pipelines are heavily utilizing ensemble learning strategies

An Analysis on Ensemble Learning optimized Medical Image Classification with Deep Convolutional Neural Networks Novel and high-performance medical ima

14 Dec 18, 2022
Nonnegative spatial factorization for multivariate count data

Nonnegative spatial factorization for multivariate count data This repository contains supporting code to facilitate reproducible analysis. For detail

Will Townes 24 Dec 19, 2022
This code implements constituency parse tree aggregation

README This code implements constituency parse tree aggregation. Folder details code: This folder contains the code that implements constituency parse

Adithya Kulkarni 0 Oct 11, 2021
Implementation of SSMF: Shifting Seasonal Matrix Factorization

SSMF Implementation of SSMF: Shifting Seasonal Matrix Factorization, Koki Kawabata, Siddharth Bhatia, Rui Liu, Mohit Wadhwa, Bryan Hooi. NeurIPS, 2021

Koki Kawabata 9 Jun 10, 2022
Repo for "Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks"

Summary This is the code for the paper Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks by Yanxiang Wang, Xian Zh

zhangxian 54 Jan 03, 2023
Turn based roguelike in python

pyTB Turn based roguelike in python Documentation can be found here: http://mcgillij.github.io/pyTB/index.html Screenshot Dependencies Written in Pyth

Jason McGillivray 4 Sep 29, 2022
Deep deconfounded recommender (Deep-Deconf) for paper "Deep causal reasoning for recommendations"

Deep Causal Reasoning for Recommender Systems The codes are associated with the following paper: Deep Causal Reasoning for Recommendations, Yaochen Zh

Yaochen Zhu 22 Oct 15, 2022
structured-generative-modeling

This repository contains the implementation for the paper Information Theoretic StructuredGenerative Modeling, Specially thanks for the open-source co

0 Oct 11, 2021
Predictive AI layer for existing databases.

MindsDB is an open-source AI layer for existing databases that allows you to effortlessly develop, train and deploy state-of-the-art machine learning

MindsDB Inc 12.2k Jan 03, 2023
Simple and Robust Loss Design for Multi-Label Learning with Missing Labels

Simple and Robust Loss Design for Multi-Label Learning with Missing Labels Official PyTorch Implementation of the paper Simple and Robust Loss Design

Xinyu Huang 28 Oct 27, 2022