Deep neuroevolution of recurrent and discrete world models

Risi, Sebastian; Stanley, Kenneth O.

doi:10.1145/3321707.3321817

Cited by 41 publications

(32 citation statements)

References 33 publications

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“…Gaming similarly to DRL, gaming represents one of the main testbeds for ES. Most of the literature on ES reviewed in this survey test their algorithms on Atari games [4,5,29,32,256,257,258,259]. These are considered to be challenging as they present the agents with high dimensional visual inputs and a diverse and interesting set of tasks that were designed to be difficult for humans players [14].…”

Section: B Evolution Strategy Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹,

Saaybi²,

Rietbergen³

et al. 2021

Preprint

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<div>Deep Reinforcement Learning (DRL) has the potential to surpass human-level control in sequential decision-making problems. Evolution Strategies (ESs) have different characteristics than DRL, yet they are promoted as a scalable alternative. </div><div>To get insights into their strengths and weaknesses, in this paper, we put the two approaches side by side. After presenting the fundamental concepts and algorithms for each of the two approaches, they are compared from the perspectives of scalability, exploration, adaptation to dynamic environments, and multi-agent learning. Then, the paper discusses hybrid algorithms, combining aspects of both DRL and ESs, and how they attempt to capitalize on the benefits of both techniques. Lastly, both approaches are compared based on the set of applications they support, showing their potential for tackling real-world problems.</div><div>This paper aims to present an overview of how DRL and ESs can be used, either independently or in unison, to solve specific learning tasks. It is intended to guide researchers to select which method suits them best and provides a bird's eye view of the overall literature in the field. Further, we also provide application scenarios and open challenges. </div>

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Section: B Evolution Strategy Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹,

Saaybi²,

Rietbergen³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…similarly to DRL, gaming represents one of the main testbeds for ESs. Most of the literature on ESs reviewed in this survey test their algorithms on Atari games [4,5,29,32,226,227,228,229]. These are considered to be challenging as they present the agents with high dimensional visual inputs and a diverse and interesting set of tasks that were designed to be difficult for humans players [14].…”

Section: B Evolution Strategy Applicationsmentioning

confidence: 99%

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Majid¹,

Saaybi²,

Rietbergen³

et al. 2021

Preprint

View full text Add to dashboard Cite

<div>Deep Reinforcement Learning (DRL) and Evolution Strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist.</div><div>To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. </div><div>After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects such as scalability, exploration, adaptation to dynamic environments, and multi-agent learning. </div><div>Then, the benefits of hybrid algorithms that combine concepts from DRL and ESs are highlighted. </div><div>Finally, to have an indication about how they compare in real-world applications, a survey of the literature for the set of applications they support is provided.</div>

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“…Interestingly, this approach also allowed an agent to get better by training inside a hallucinated environment created through a trained world model. Instead of first training a policy on random rollouts, follow-up work showed that end-to-end learning through reinforcement learning [28] and evolution [65,66] is also possible. We will discuss MuZero as another example of planning in latent space in Section 6.…”

Section: Learning To Play From Pixelsmentioning

confidence: 99%

From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI

Risi

Preuß

2020

Künstl Intell

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This paper reviews the field of Game AI, which not only deals with creating agents that can play a certain game, but also with areas as diverse as creating game content automatically, game analytics, or player modelling. While Game AI was for a long time not very well recognized by the larger scientific community, it has established itself as a research area for developing and testing the most advanced forms of AI algorithms and articles covering advances in mastering video games such as StarCraft 2 and Quake III appear in the most prestigious journals. Because of the growth of the field, a single review cannot cover it completely. Therefore, we put a focus on important recent developments, including that advances in Game AI are starting to be extended to areas outside of games, such as robotics or the synthesis of chemicals. In this article, we review the algorithms and methods that have paved the way for these breakthroughs, report on the other important areas of Game AI research, and also point out exciting directions for the future of Game AI.

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Deep neuroevolution of recurrent and discrete world models

Cited by 41 publications

References 33 publications

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey

From Chess and Atari to StarCraft and Beyond: How Game AI is Driving the World of AI

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