8 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 )
These leaderboards are used to track progress in Dota 2
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.
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.
This poses non-trivial difficulties for researchers or engineers and prevents the application of MARL to a broader range of real-world problems.
Prediction of the real-time multiplayer online battle arena (MOBA) games' match outcome is one of the most important and exciting tasks in Esports analytical research.