DOCSLIB.ORG

Improving Model-Based Reinforcement Learning with Internal State Representations Through Self-Supervision

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Improving Model-Based Reinforcement Learning with Internal State Representations Through Self-Supervision 1 Improving Model-Based Reinforcement Learning with Internal State Representations through Self-Supervision Julien Scholz, Cornelius Weber, Muhammad Burhan Hafez and Stefan Wermter Knowledge Technology, Department of Informatics, University of Hamburg, Germany [email protected], fweber, hafez, [email protected] Abstract—Using a model of the environment, reinforcement the design decisions made for MuZero, namely, how uncon- learning agents can plan their future moves and achieve super- strained its learning process is. After explaining all necessary human performance in board games like Chess, Shogi, and Go, fundamentals, we propose two individual augmentations to while remaining relatively sample-efficient. As demonstrated by the MuZero Algorithm, the environment model can even be the algorithm that work fully unsupervised in an attempt to learned dynamically, generalizing the agent to many more tasks improve its performance and make it more reliable. After- while at the same time achieving state-of-the-art performance. ward, we evaluate our proposals by benchmarking the regular Notably, MuZero uses internal state representations derived from MuZero algorithm against the newly augmented creation. real environment states for its predictions. In this paper, we bind the model’s predicted internal state representation to the environ- II. BACKGROUND ment state via two additional terms: a reconstruction model loss and a simpler consistency loss, both of which work independently A. MuZero Algorithm and unsupervised, acting as constraints to stabilize the learning process. Our experiments show that this new integration of The MuZero algorithm [1] is a model-based reinforcement reconstruction model loss and simpler consistency loss provide a learning algorithm that builds upon the success of its prede- significant performance increase in OpenAI Gym environments. cessor, AlphaZero [9], which itself is a generalization of the Our modifications also enable self-supervised pretraining for successful AlphaGo [10] and AlphaGo Zero [11] algorithms MuZero, so the algorithm can learn about environment dynamics before a goal is made available. to a broader range of challenging board and Atari games. Similar to other model-based algorithms, MuZero can predict I. INTRODUCTION the behavior of its environment to plan and choose the most Reinforcement learning algorithms are classified into two promising action at each timestep to achieve its goal. In groups. Model-free algorithms can be viewed as behaving contrast to AlphaZero, it does this using a learned model. instinctively, while model-based algorithms can plan their next As such, it can be applied to environments for which the moves ahead of time. The latter are arguably more human-like rules are not known ahead of time. MuZero uses an internal and have the added advantage of being comparatively sample- (or embedded) state representation that is deduced from the efficient. environment observation but is not required to have any The MuZero Algorithm [1] outperforms its competitors in semantic meaning beyond containing sufficient information to a variety of tasks while simultaneously being very sample- predict rewards and values. Accordingly, it may be infeasible efficient. Unlike other recent model-based reinforcement learn- to reconstruct observations from internal states. This gives ing approaches that perform gradient-based planning [2]–[6], MuZero an advantage over traditional model-based algorithms MuZero does not assume a continuous and differentiable that need to be able to predict future observations (e.g. noisy action space. However, we realized that MuZero performed visual input). relatively poorly on our experiments in which we trained a There are three distinct functions (e.g. neural networks) robot to grasp objects using a low-dimensional input space and that are used harmoniously for planning. Namely, there is was comparatively slow to operate. Contrary to our expecta- a representation function hθ, a prediction function fθ, as tions, older, model-free algorithms such as A3C [7] delivered well as a dynamics function gθ, each being parameterized significantly better results while being more lightweight and using parameters θ to allow for adjustment through a training comparatively easy to implement. Even in very basic tasks, process. The complete model is called µθ. We will now explain our custom implementation as well as one provided by other each of the three functions in more detail. researchers, muzero-general [8], produced unsatisfactory out- • The representation function hθ maps real observations to comes. internal state representations. For a sequence of recorded Since these results did not match those that MuZero observations o1; : : : ; ot at timestep t, an embedded rep- 0 achieved in complex board games [1], we are led to believe resentation s = h(o1; : : : ; ot) may be produced. As that MuZero may require extensive training time and expensive previously mentioned, s0 has no semantic meaning, hardware to reach its full potential. We question some of and typically contains significantly less information than © 2021 IEEE. Personal use of this material is permitted. o1; : : : ; ot. Thus, the function hθ is tasked with eliminat- ::: ot−1 ot ot+1 ::: ing unnecessary details from observations, for example by extracting object coordinates and other attributes from hθ 1 2 3 images. r r r • The dynamics function g tries to mimic the environment θ s0 gθ s1 gθ s2 gθ s3 by advancing an internal state sk−1 at a hypothetical k timestep k − 1 based on a chosen action a to predict a1 a2 a3 k k k−1 k k fθ r ; s = gθ s ; a , where r is an estimate of the k real reward ut+k and s is the internal state at timestep 3 3 k. This function can be applied recursively, and acts as p ; v the simulating part of the algorithm, estimating what may 1 2 happen when taking a sequence of actions a1; :::; ak in a Fig. 1. Testing a single action sequence made up of three actions a , a , and a3 to plan based on observation o using all three MuZero functions. state s0. t • The prediction function fθ can be compared to an actor- critic architecture having both a policy and a value output. p an estimate (that is faster to compute) of what our search k For any internal state s , there shall be a mapping policy π might be, meaning it can be used as a heuristic. For k k k k p ; v = fθ(s ), where policy p represents the prob- our value estimates ν, we first calculate n-step bootstrapped n−1 n abilities with which the agent would perform actions and returns zt = ut+1 + γut+2 + ::: + γ ut+n + γ νt+n. With k k v value v is the expected return in state s . Whereas the another loss l , the target zt+k is set for each value output value is very useful to bootstrap future rewards after k vt . The three previously mentioned output targets as well the final step of planning by the dynamics function, the as an additional L2 regularization term cjjθjj2 [12] form the policy of the prediction function may be counterintuitive, complete loss function: keeping in mind that the agent should derive its policy from the considered plans. We will explore the uses of K k X r k v k p further on. lt(θ) = l (ut+k; rt ) + l (zt+k; vt ) Given parameters θ, we can now decide on an action policy k=0 p k 2 π for each observation ot, which we will call the search policy, + l (πt+k; pt ) + cjjθjj (2) that uses hθ, gθ and fθ to search through different action sequences and find an optimal plan. As an example, in a The aforementioned brute-force search policy is highly un- small action space, we can iterate through all action sequences optimized. For one, we have not described a method of caching and reusing internal states and prediction function outputs so a1; :::; an of a fixed length n, and apply each function in the order visualized in Fig.1. By discounting the reward and as to reduce the computational footprint. Furthermore, iterating value outputs with a discount factor γ 2 [0; 1], we receive through all possible actions is very slow for a high number of an estimate for the return when first performing the action actions or longer action sequences and impossible for continu- sequence and subsequently following π: ous action spaces. Ideally, we would focus our planning efforts on only those actions that are promising from the beginning Eπ [ut+1 + γut+2 + ::: j ot; a1; : : : ; an] and spend less time incorporating clearly unfavorable behavior. n AlphaZero and MuZero employ a variation of Monte-Carlo X = γk−1rk + γnvn (1) tree search (MCTS)[13] (see Section II-B). k=1 MuZero matches the state-of-the-art results of AlphaZero Given the goal of an agent to maximize the return, we may in the board games Chess and Shogi, despite not having now simply choose the first action of the action sequence with access to a perfect environment model. It even exceeded the highest return estimate. Alternatively, a less promising AlphaZero’s unprecedented rating in the board game Go, action can be selected to encourage the exploration of new while using less computation per node, suggesting that it behavior. At timestep t, we call the return estimate for the caches useful information in its internal states to gain a chosen action produced by our search νt. deeper understanding of the environment with each application Training occurs on trajectories previously observed by the of the dynamics function. Furthermore, MuZero outperforms MuZero agent. For each timestep t in the trajectory, we unroll humans and previous state-of-the-art agents at the Atari game the dynamics function K steps through time and adjust θ such benchmark, demonstrating its ability to solve tasks with long that the predictions further match what was already observed time horizons that are not turn-based.
Load more

Recommended publications
  • Survey on Reinforcement Learning for Language Processing
    Survey on reinforcement learning for language processing V´ıctorUc-Cetina1, Nicol´asNavarro-Guerrero2, Anabel Martin-Gonzalez1, Cornelius Weber3, Stefan Wermter3 1 Universidad Aut´onomade Yucat´an- fuccetina, [email protected] 2 Aarhus University - [email protected] 3 Universit¨atHamburg - fweber, [email protected] February 2021 Abstract In recent years some researchers have explored the use of reinforcement learning (RL) algorithms as key components in the solution of various natural language process- ing tasks. For instance, some of these algorithms leveraging deep neural learning have found their way into conversational systems. This paper reviews the state of the art of RL methods for their possible use for different problems of natural language processing, focusing primarily on conversational systems, mainly due to their growing relevance. We provide detailed descriptions of the problems as well as discussions of why RL is well-suited to solve them. Also, we analyze the advantages and limitations of these methods. Finally, we elaborate on promising research directions in natural language processing that might benefit from reinforcement learning. Keywords| reinforcement learning, natural language processing, conversational systems, pars- ing, translation, text generation 1 Introduction Machine learning algorithms have been very successful to solve problems in the natural language pro- arXiv:2104.05565v1 [cs.CL] 12 Apr 2021 cessing (NLP) domain for many years, especially supervised and unsupervised methods. However, this is not the case with reinforcement learning (RL), which is somewhat surprising since in other domains, reinforcement learning methods have experienced an increased level of success with some impressive results, for instance in board games such as AlphaGo Zero [106].
    [Show full text]
  • Multi-Agent Reinforcement Learning: a Review of Challenges and Applications
    applied sciences Review Multi-Agent Reinforcement Learning: A Review of Challenges and Applications Lorenzo Canese †, Gian Carlo Cardarilli, Luca Di Nunzio †, Rocco Fazzolari † , Daniele Giardino †, Marco Re † and Sergio Spanò *,† Department of Electronic Engineering, University of Rome “Tor Vergata”, Via del Politecnico 1, 00133 Rome, Italy; [email protected] (L.C.); [email protected] (G.C.C.); [email protected] (L.D.N.); [email protected] (R.F.); [email protected] (D.G.); [email protected] (M.R.) * Correspondence: [email protected]; Tel.: +39-06-7259-7273 † These authors contributed equally to this work. Abstract: In this review, we present an analysis of the most used multi-agent reinforcement learn- ing algorithms. Starting with the single-agent reinforcement learning algorithms, we focus on the most critical issues that must be taken into account in their extension to multi-agent scenarios. The analyzed algorithms were grouped according to their features. We present a detailed taxonomy of the main multi-agent approaches proposed in the literature, focusing on their related mathematical models. For each algorithm, we describe the possible application fields, while pointing out its pros and cons. The described multi-agent algorithms are compared in terms of the most important char- acteristics for multi-agent reinforcement learning applications—namely, nonstationarity, scalability, and observability. We also describe the most common benchmark environments used to evaluate the Citation: Canese, L.; Cardarilli, G.C.; performances of the considered methods. Di Nunzio, L.; Fazzolari, R.; Giardino, D.; Re, M.; Spanò, S. Multi-Agent Keywords: machine learning; reinforcement learning; multi-agent; swarm Reinforcement Learning: A Review of Challenges and Applications.
    [Show full text]
  • Combining Off and On-Policy Training in Model-Based Reinforcement Learning
    Combining Off and On-Policy Training in Model-Based Reinforcement Learning Alexandre Borges Arlindo L. Oliveira INESC-ID / Instituto Superior Técnico INESC-ID / Instituto Superior Técnico University of Lisbon University of Lisbon Lisbon, Portugal Lisbon, Portugal ABSTRACT factor and game length. AlphaGo [1] was the first computer pro- The combination of deep learning and Monte Carlo Tree Search gram to beat a professional human player in the game of Go by (MCTS) has shown to be effective in various domains, such as combining deep neural networks with tree search. In the first train- board and video games. AlphaGo [1] represented a significant step ing phase, the system learned from expert games, but a second forward in our ability to learn complex board games, and it was training phase enabled the system to improve its performance by rapidly followed by significant advances, such as AlphaGo Zero self-play using reinforcement learning. AlphaGoZero [2] learned [2] and AlphaZero [3]. Recently, MuZero [4] demonstrated that only through self-play and was able to beat AlphaGo soundly. Alp- it is possible to master both Atari games and board games by di- haZero [3] improved on AlphaGoZero by generalizing the model rectly learning a model of the environment, which is then used to any board game. However, these methods were designed only with Monte Carlo Tree Search (MCTS) [5] to decide what move for board games, specifically for two-player zero-sum games. to play in each position. During tree search, the algorithm simu- Recently, MuZero [4] improved on AlphaZero by generalizing lates games by exploring several possible moves and then picks the it even more, so that it can learn to play singe-agent games while, action that corresponds to the most promising trajectory.
    [Show full text]
  • On Building Generalizable Learning Agents
    On Building Generalizable Learning Agents Yi Wu Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2020-9 http://www2.eecs.berkeley.edu/Pubs/TechRpts/2020/EECS-2020-9.html January 10, 2020 Copyright © 2020, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. On Building Generalizable Learning Agents by Yi Wu A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Computer Science in the Graduate Division of the University of California, Berkeley Committee in charge: Professor Stuart Russell, Chair Professor Pieter Abbeel Associate Professor Javad Lavaei Assistant Professor Aviv Tamar Fall 2019 On Building Generalizable Learning Agents Copyright 2019 by Yi Wu 1 Abstract On Building Generalizable Learning Agents by Yi Wu Doctor of Philosophy in Computer Science University of California, Berkeley Professor Stuart Russell, Chair It has been a long-standing goal in Artificial Intelligence (AI) to build machines that can solve tasks that humans can. Thanks to the recent rapid progress in data-driven methods, which train agents to solve tasks by learning from massive training data, there have been many successes in applying such learning approaches to handle and even solve a number of extremely challenging tasks, including image classification, language generation, robotics control, and several complex multi-player games.
    [Show full text]
  • On the Role of Planning in Model-Based Deep
    Published as a conference paper at ICLR 2021 ON THE ROLE OF PLANNING IN MODEL-BASED DEEP REINFORCEMENT LEARNING Jessica B. Hamrick,∗ Abram L. Friesen, Feryal Behbahani, Arthur Guez, Fabio Viola, Sims Witherspoon, Thomas Anthony, Lars Buesing, Petar Velickoviˇ c,´ Theophane´ Weber∗ DeepMind, London, UK ABSTRACT Model-based planning is often thought to be necessary for deep, careful reason- ing and generalization in artificial agents. While recent successes of model-based reinforcement learning (MBRL) with deep function approximation have strength- ened this hypothesis, the resulting diversity of model-based methods has also made it difficult to track which components drive success and why. In this paper, we seek to disentangle the contributions of recent methods by focusing on three questions: (1) How does planning benefit MBRL agents? (2) Within planning, what choices drive performance? (3) To what extent does planning improve gen- eralization? To answer these questions, we study the performance of MuZero [58], a state-of-the-art MBRL algorithm with strong connections and overlapping com- ponents with many other MBRL algorithms. We perform a number of interven- tions and ablations of MuZero across a wide range of environments, including control tasks, Atari, and 9x9 Go. Our results suggest the following: (1) Planning is most useful in the learning process, both for policy updates and for providing a more useful data distribution. (2) Using shallow trees with simple Monte-Carlo rollouts is as performant as more complex methods, except in the most difficult reasoning tasks. (3) Planning alone is insufficient to drive strong generalization. These results indicate where and how to utilize planning in reinforcement learning settings, and highlight a number of open questions for future MBRL research.
    [Show full text]
  • The Evolving Electric Power Grid -Energy Internet, Iot and AI
    The Evolving Electric Power Grid -Energy Internet, IoT and AI Di Shi AI & System Analytics GEIRI North America (GEIRINA) www.geirina.net San Jose, CA, US May 8, 2020 @CURENT Strategic Planning Meeting Outline 1. Recent Development: Energy Internet and IoT 2. The New-gen. AI Technologies 3. Challenges and Opportunities Unbalance in Resources and Load Resource 76% of coal in North and Northwest 80% of hydropower in Southwest, mainly in upper stream of Yangtze River All inland wind in Northwest Solar resources mainly in Northwest Load 70%+ of load in Central and Eastern parts Fig. West to east power transmission Transmission Distances between resource and load center reaches up to 2000+km UHVDC and UHVAC are good options to transfer huge amount of power over long distance Challenges Hybrid operation of AC and DC systems System stability, security, and reliability Fig. China power transmission framework 3 4 Wind Solar 18,000MW 30,120MW 48,200MW 19.400MW Thermal 42,200MW 32.400MW 1,000MW Hydro 8,000MW 20,000MW 2,000MW 8,000MW HVDC HVDC or HVAC 28% Investment/MW UHVDC 50% Loss/MW UHVDC Developmental Trend: Source-Grid-Load Interaction Source-Grid-load interaction proposed by SGCC Jiangsu UHVDC/UHVAC/HVDC/HVAC development in China Technical aspect: Holistic generation- transmission-demand coordination Conventional Conventional load generation Pumped- Energy storage storage Source Grid Load Renewable generation EV/PHEV Battery energy storage Power electronics interfaced load Market aspect: real-time pricing, incentives, etc The existing load control methods are of low UHVDC Fault Caused Outage in Brazil granularity, and the load response is slow.
    [Show full text]
  • Monte-Carlo Tree Search As Regularized Policy Optimization
    Monte-Carlo tree search as regularized policy optimization Jean-Bastien Grill * 1 Florent Altche´ * 1 Yunhao Tang * 1 2 Thomas Hubert 3 Michal Valko 1 Ioannis Antonoglou 3 Remi´ Munos 1 Abstract AlphaZero employs an alternative handcrafted heuristic to achieve super-human performance on board games (Silver The combination of Monte-Carlo tree search et al., 2016). Recent MCTS-based MuZero (Schrittwieser (MCTS) with deep reinforcement learning has et al., 2019) has also led to state-of-the-art results in the led to significant advances in artificial intelli- Atari benchmarks (Bellemare et al., 2013). gence. However, AlphaZero, the current state- of-the-art MCTS algorithm, still relies on hand- Our main contribution is connecting MCTS algorithms, crafted heuristics that are only partially under- in particular the highly-successful AlphaZero, with MPO, stood. In this paper, we show that AlphaZero’s a state-of-the-art model-free policy-optimization algo- search heuristics, along with other common ones rithm (Abdolmaleki et al., 2018). Specifically, we show that such as UCT, are an approximation to the solu- the empirical visit distribution of actions in AlphaZero’s tion of a specific regularized policy optimization search procedure approximates the solution of a regularized problem. With this insight, we propose a variant policy-optimization objective. With this insight, our second of AlphaZero which uses the exact solution to contribution a modified version of AlphaZero that comes this policy optimization problem, and show exper- significant performance gains over the original algorithm, imentally that it reliably outperforms the original especially in cases where AlphaZero has been observed to algorithm in multiple domains.
    [Show full text]
  • Reinforcement Learning in Stock Market
    Master Thesis Reinforcement Learning in Stock Market Author: Supervisors: Pablo Carrera Fl´orezde Qui~nones Valero Laparra P´erez-Muelas Jordi Mu~nozMar´ı Abstract Reinforcement Learning is a very active field that has seen lots of progress in the last decade, however industry is still resistant to incorporate it to the automatization of its decision process. In this work we make a revision of the fundamentals of Reinforcement Learning reaching some state-of-the-art algorithms in order to make its implementation more profitable. As a practical application of this knowledge, we propose a Reinforcement Learning approach to Stock Market from scratch, developing an original agent-environment interface and comparing the performance of different training algorithms. Finally we run a series of experiments to tune our agent and test its performance. Resumen El Reinforcement Learning es un campo muy activo que ha sufrido grandes progresos en la ´ultimadecada, sin embargo la industria todav´ıase muestra reacia a incorporarlo a la autom- atizaci´onde su proceso de toma de decisiones. En este trabajo hacemos una revisi´onde los fundamentos del Reinforcement Learning alcanzando el estado del arte en algunos de los algorith- mos desarrollados para hacer su implementaci´onm´asprometedora. Como aplicaci´onpr´acticade este conocimiento proponemos una aproximacion desde cero al Mercado de Valores usando Rein- forcement Learning, desarrollando una interfaz agent-entorno original y comparando el desempe~no de diferentes algoritmos de entrenamiento. Finalmente realizamos una serie de experimentos para tunear y testear el desempe~node nuestro agente. Resum El Reinforcement Learning ´esun camp molt actiu que ha patit grans progressos en l'´ultima d`ecada,no obstant aix`ola ind´ustriaencara es mostra poc inclinada a incorporar-ho a l'automatitzaci´o del seu proc´esde presa de decisions.
    [Show full text]
  • Playing Nondeterministic Games Through Planning with a Learned
    Under review as a conference paper at ICLR 2021 PLAYING NONDETERMINISTIC GAMES THROUGH PLANNING WITH A LEARNED MODEL Anonymous authors Paper under double-blind review ABSTRACT The MuZero algorithm is known for achieving high-level performance on tradi- tional zero-sum two-player games of perfect information such as chess, Go, and shogi, as well as visual, non-zero sum, single-player environments such as the Atari suite. Despite lacking a perfect simulator and employing a learned model of environmental dynamics, MuZero produces game-playing agents comparable to its predecessor, AlphaZero. However, the current implementation of MuZero is restricted only to deterministic environments. This paper presents Nondetermin- istic MuZero (NDMZ), an extension of MuZero for nondeterministic, two-player, zero-sum games of perfect information. Borrowing from Nondeterministic Monte Carlo Tree Search and the theory of extensive-form games, NDMZ formalizes chance as a player in the game and incorporates the chance player into the MuZero network architecture and tree search. Experiments show that NDMZ is capable of learning effective strategies and an accurate model of the game. 1 INTRODUCTION While the AlphaZero algorithm achieved superhuman performance in a variety of challenging do- mains, it relies upon a perfect simulation of the environment dynamics to perform precision plan- ning. MuZero, the newest member of the AlphaZero family, combines the advantages of planning with a learned model of its environment, allowing it to tackle problems such as the Atari suite without the advantage of a simulator. This paper presents Nondeterministic MuZero (NDMZ), an extension of MuZero to stochastic, two-player, zero-sum games of perfect information.
    [Show full text]
  • Yaklaşim Ve Uygulamalar
    YAPAY ZEKÂ YAKLAŞIM VE UYGULAMALAR OUTLOOK | EYLÜL 2020 STM THINKTECH OUTLOOK | AĞUSTOS 2020 YAPAY ZEKÂ YAKLAŞIM VE UYGULAMALAR Yapay zekâ ekonomiden sosyal hayata yaşamın her alanında temel belirleyici dinamiklerden birine dönüştü. Outlook raporumuzda yapay zekânın tüm alanlarda yarattığı değişimi dünyanın bu alanda önde gelen uzmanlarının görüşleri çerçevesinde kapsamlı bir şekilde inceledik. 3 » Yapay Zekâ Nedir? 6 » Tüm Dünyada Yaşanan Yapay Zekâ Patlaması 11 » 2019’daki Yapay Zekâ Alanındaki Gelişmeler Üzerine Düşünceler 15 » Yapay Zekâ ve Jeopolitik Yarış 18 » Yapay Zekâ ve Ekonomi 25 » Yapay Zekâ ve Eğitim 28 » Yapay Zekâ ve Sağlık 31 » Yapay Zekâ ve İklim Değişikliği 34 » Yapay Zekâ, Savunma ve Güvenlik 42 » Yapay Zekâ ve Etik Tartışmaları 48 » İyilik Gücü Olarak Yapay Zekâ 53 » Yapay Zekâya ve Geleceğine İlişkin Tartışmalar 65 » Yapay Zekâ ve COVID-19 69 » Kaynakça İşbu eserde yer alan veriler/bilgiler, yalnızca bilgi amaçlı olup, bu eserde bulunan veriler/bilgiler tavsiye, reklam ya da iş geliştirme amacına yönelik değildir. STM Savunma Teknolojileri Mühendislik ve Ticaret A.Ş. işbu eserde sunulan verilerin/ bilgilerin içeriği, güncelliği ya da doğruluğu konusunda herhangi bir taahhüde girmemekte, kullanıcı veya üçüncü kişilerin bu eserde yer alan verilere/bilgilere dayanarak gerçekleştirecekleri eylemlerden ötürü sorumluluk kabul etmemektedir. Bu eserde yer alan bilgilerin her türlü hakkı STM Savunma Teknolojileri Mühendislik ve Ticaret A.Ş’ye aittir. Yazılı izin olmaksızın işbu eserde yer alan bilgi, yazı, ifadenin bir kısmı veya tamamı, herhangi bir ortamda hiçbir şekilde yayımlanamaz, çoğaltılamaz, işlenemez. STM THINKTECH OUTLOOK | YAPAY ZEKÂ - YAKLAŞIM VE UYGULAMALAR GİRİŞ YAPAY ZEKÂ NEDİR? Yapay zekâ, ister makine öğrenmesi kullansın ister kullanmasın herhangi bir tahmin veya karar işlemini gerçekleştiren teknolojilerin genel adıdır.
    [Show full text]
  • Muesli: Combining Improvements in Policy Optimization
    Muesli: Combining Improvements in Policy Optimization Matteo Hessel * 1 Ivo Danihelka * 1 2 Fabio Viola 1 Arthur Guez 1 Simon Schmitt 1 Laurent Sifre 1 Theophane Weber 1 David Silver 1 2 Hado van Hasselt 1 Abstract atari57 median atari57 median Muesli MuZero[2021] TRPO penalty 1000% Muesli We propose a novel policy update that combines 400% PPO regularized policy optimization with model learn- PG 800% MPO ing as an auxiliary loss. The update (henceforth 300% Muesli) matches MuZero’s state-of-the-art perfor- 600% mance on Atari. Notably, Muesli does so without 200% using deep search: it acts directly with a policy 400% LASER network and has computation speed comparable 100% 200% Rainbow to model-free baselines. The Atari results are score human-normalized Median complemented by extensive ablations, and by ad- 0% 0% 0 50 100 150 200 0 50 100 150 200 ditional results on continuous control and 9x9 Go. Millions of frames Millions of frames (a) (b) Figure 1. Median human normalized score across 57 Atari 1. Introduction games. (a) Muesli and other policy updates; all these use the same IMPALA network and a moderate amount of replay data Reinforcement learning (RL) is a general formulation for the (75%). Shades denote standard errors across 5 seeds. (b) Muesli problem of sequential decision making under uncertainty, with the larger MuZero network and the high replay fraction used where a learning system (the agent) must learn to maximize by MuZero (95%), compared to the latest version of MuZero. the cumulative rewards provided by the world it is embed- These large scale runs use 2 seeds.
    [Show full text]
  • The Impact of Artificial Intelligence on the Chess World
    JMIR SERIOUS GAMES Duca Iliescu Viewpoint The Impact of Artificial Intelligence on the Chess World Delia Monica Duca Iliescu, BSc, MSc Transilvania University of Brasov, Brasov, Romania Corresponding Author: Delia Monica Duca Iliescu, BSc, MSc Transilvania University of Brasov Bdul Eroilor 29 Brasov Romania Phone: 40 268413000 Email: [email protected] Abstract This paper focuses on key areas in which artificial intelligence has affected the chess world, including cheat detection methods, which are especially necessary recently, as there has been an unexpected rise in the popularity of online chess. Many major chess events that were to take place in 2020 have been canceled, but the global popularity of chess has in fact grown in recent months due to easier conversion of the game from offline to online formats compared with other games. Still, though a game of chess can be easily played online, there are some concerns about the increased chances of cheating. Artificial intelligence can address these concerns. (JMIR Serious Games 2020;8(4):e24049) doi: 10.2196/24049 KEYWORDS artificial intelligence; games; chess; AlphaZero; MuZero; cheat detection; coronavirus strategic games, has affected many other areas of interest, as Introduction has already been seen in recent years. All major chess events that were to take place in the second half One of the most famous human versus machine events was the of 2020 have been canceled, including the 44th Chess Olympiad 1997 victory of Deep Blue, an IBM chess software, against the and the match for the title of World Chess Champion. However, famous chess champion Garry Kasparov [3].
    [Show full text]