Dota 2

6 papers with code • 0 benchmarks • 0 datasets

Dota 2 is a multiplayer online battle arena (MOBA). The task is to train one-or-more agents to play and win the game.

( Image credit: OpenAI Five )


Greatest papers with code

Proximal Policy Optimization Algorithms

hill-a/stable-baselines 20 Jul 2017

We propose a new family of policy gradient methods for reinforcement learning, which alternate between sampling data through interaction with the environment, and optimizing a "surrogate" objective function using stochastic gradient ascent.

Dota 2 Policy Gradient Methods

An Empirical Model of Large-Batch Training

astooke/rlpyt 14 Dec 2018

In an increasing number of domains it has been demonstrated that deep learning models can be trained using relatively large batch sizes without sacrificing data efficiency.

Dota 2

Multi-Agent Collaboration via Reward Attribution Decomposition

facebookresearch/CollaQ 16 Oct 2020

In this work, we propose Collaborative Q-learning (CollaQ) that achieves state-of-the-art performance in the StarCraft multi-agent challenge and supports ad hoc team play.

Dota 2 Multi-agent Reinforcement Learning +2

TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement Learning

tencent-ailab/tleague_projpage 25 Nov 2020

This poses non-trivial difficulties for researchers or engineers and prevents the application of MARL to a broader range of real-world problems.

Dota 2 Multi-agent Reinforcement Learning +2

Time to Die: Death Prediction in Dota 2 using Deep Learning

adam-katona/dota2_death_prediction 21 May 2019

Even though death events are rare within a game (1\% of the data), the model achieves 0. 377 precision with 0. 725 recall on test data when prompted to predict which of any of the 10 players of either team will die within 5 seconds.

Dota 2

Real-time eSports Match Result Prediction

yang1fan2/Dota2-Prediction 10 Dec 2016

In this paper, we try to predict the winning team of a match in the multiplayer eSports game Dota 2.

Dota 2