Large Scale Deep Reinforcement Learning in War|games

Dota 2 with Large Scale Deep Reinforcement Learning

the Dota 2 community for competitive play; OpenAI Five won 99.4% of over 7000 games. One challenge we faced in training was that the environment ...

15 awesome reinforcement learning environments you must know

Gym-μRTS is similar to Deep RTS in objective. Existing full games have high computational costs which usually translates to thousands of hours ...

Dota 2 with Large Scale Deep Reinforcement Learning - OpenAI

The long-term goal of artificial intelligence is to solve advanced real-world challenges. Games have served as stepping stones along this path ...

Mastering Real-Time Strategy Games with Deep Reinforcement ...

[11] describes a large-scale distributed-systems architecture for competitive self-play based multi-agent reinforcement learning which is ...

Scalable Deep Reinforcement Learning Algorithms for Mean Field ...

Mean Field Games (MFGs) have been intro- duced to efficiently approximate games with very large populations of strategic agents. Recently,.

Playing Atari with Deep Reinforcement Learning

Firstly, most successful deep learning applications to date have required large amounts of hand- labelled training data. RL algorithms, on the other hand, must ...

Mastering Complex Control in MOBA Games with Deep ...

Mastering Complex Control in MOBA Games with Deep Reinforcement Learning ... (4) to penalize extreme changes to the policy. However, in large-scale off-policy ...

Measuring Generalization of Deep Reinforcement Learning Applied ...

Dota 2 with Large Scale. Deep Reinforcement Learning. ArXiv abs/1912.06680. Buro, M. 2003. Real-time strategy games: A new AI re- search challenge. In IJCAI ...

Towards Playing Full MOBA Games with Deep Reinforcement ...

We propose a novel MOBA AI learning paradigm towards playing full MOBA games with deep reinforcement learning. • We conduct the first large-scale performance ...

Mastering Complex Control in MOBA Games with Deep ... - ar5iv

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 ...

Using Deep Reinforcement Learning to Generalize Search in Games

Search methods have been instrumental in computing superhuman strategies for large-scale games [1,2,3]. However, existing search techniques are tabular and ...

Multi-agent deep reinforcement learning: a survey

However, it has been shown that agents can converge, despite a high degree of randomness in action selection, to sub-optimal solutions or can ...

Deep Reinforcement Learning: Its Tech and Applications - viso.ai

Deep RL has achieved human-level or superhuman performance for many two-player or even multi-player games. Such achievements with popular games ...

Deep Reinforcement Learning for Navigation in AAA Video Games

As an alternative, we propose to use Deep Reinforcement Learning (Deep RL) to learn how to navigate on 3D maps using any navigation abilities. We tested our ...

Scale-Invariant Reinforcement Learning in Real-Time Strategy Games

Real-time strategy games present a significant challenge for artificial game-playing agents by combining several fundamental AI problems.

Scalable Deep Reinforcement Learning Algorithms for Mean Field ...

Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents. Recently, the question of ...

Combining Deep Reinforcement Learning and Search for Imperfect ...

large-scale games and defeats a top human professional with statistical significance in the benchmark game of heads-up no-limit Texas hold'em poker while ...

Combining Deep Reinforcement Learning and Search for Imperfect ...

The combination of deep reinforcement learning and search at both training and test time is a powerful paradigm that has led to a number of ...

Deep Reinforcement Learning from Self-Play in Imperfect ...

Many real-world applications can be described as large-scale games of imperfect information. To deal with these challenging domains, prior.

Scaling Reinforcement Learning through Feudal Multi-Agent Hierarchy

in war games, information that is critical for decision-making is limited. ... The FMH training method used for this thesis is not acceptable for large scale.