Large Scale Deep Reinforcement Learning in War|games

Large Scale Deep Reinforcement Learning in War-games

We propose a hierarchical multi-agent reinforcement learning framework to rapidly training an AI model for the war-game. The higher-level network in our ...

Large Scale Deep Reinforcement Learning in War-games

War-game is a type of multi-agent real-time strategy game, with challenges of the large-scale decision-making space and the flexible and changeable ...

Large Scale Deep Reinforcement Learning in War-games

Large Scale Deep Reinforcement Learning in. War-games. Hanchao Wang. College of Intelligence and Computing. Tianjin University. Tianjin, China [email protected].

Large Scale Deep Reinforcement Learning in War-games

A hierarchical multi-agent reinforcement learning framework to rapidly training an AI model for the war-game based on hexagon grids is proposed and results ...

Large Scale Deep Reinforcement Learning in War-games

In Reference [57] , a hierarchical multi-agent reinforcement learning framework was proposed to train an AI model in a traditional wargame played on a hexagon ...

I Made 1.000 A.I Warriors FIGHT... (Deep Reinforcement Learning)

I trained 1.000 AI warriors to fight each-other! This is my biggest and most difficult project so far, so don't forget to support the video ...

[R] Mastering Real-Time Strategy Games with Deep Reinforcement ...

A route that I've found potentially promising is to split the single policy into two, an overlord/strategic policy and a drone/tactical policy.

Artificial intelligence can learn to play a complex war game - Sciworthy

Reinforcement learning works well in games like Backgammon, but a large-scale strategy game is much more complicated. In order to show the ...

[D] What is your honest experience with reinforcement learning?

Traditional SOTA RL algorithms like PPO, DDPG, and TD3 are just very hard. You need to do a bunch of research to even implement a toy problem.

Mastering air combat game with deep reinforcement learning

Reinforcement learning has been applied to air combat problems in recent years, and the idea of curriculum learning is often used for reinforcement learning ...

Efficient and scalable reinforcement learning for large-scale network ...

However, previous research on model-based MARL is primarily restricted to specific scenarios, for example, two-player zero-sum games, the ...

Mastering Complex Control in MOBA Games with Deep ... - arXiv

In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. Our ...

Coordinating Multi-Agent Deep Reinforcement Learning in Wargame

The successful application of deep reinforcement learning in RTS games such as StarCraft II has inspired people to apply multi-agent deep ...

Deep reinforcement learning for real time strategy games

I experimented with applications of reinforcement learning to various games, and I learned that the design of the way that the agent interacts ...

Towards Playing Full MOBA Games with Deep Reinforcement ...

In this paper, we propose a MOBA AI learning paradigm that methodologically enables playing full MOBA games with deep reinforcement learning.

Tractable large-scale deep reinforcement learning - ScienceDirect

Reinforcement learning (RL) has emerged as one of the most promising and powerful techniques in deep learning. The training of intelligent agents requires a ...

Mastering Complex Control in MOBA Games with Deep ...

In this paper, we present a deep reinforcement learning framework to tackle this problem from the perspectives of both system and algorithm. Our system is of ...

Large-scale deep learning to augment production RL ... - YouTube

Large-scale deep learning to augment production RL workloads at Riot Games At Riot Games, we leverage large-scale deep reinforcement ...

[PDF] Towards Playing Full MOBA Games with Deep Reinforcement ...

Towards Playing Full MOBA Games with Deep Reinforcement Learning · Deheng Ye, Guibin Chen, +15 authors. Wei Liu · Published in Neural Information Processing… 25 ...

Exploring Deep Reinforcement Learning for Battling in Collectible ...

Abstract—Collectible card games (CCGs), such as Magic: the. Gathering and Hearthstone, are a challenging domain where game-playing AI arguably has not yet ...