作者: Introduction 時間: 2025-3-21 21:23 作者: conduct 時間: 2025-3-22 01:07
Least-Squares Methods for Policy Iterationor the overall resulting approximate policy iteration, we provide guarantees on the performance obtained asymptotically, as the number of samples processed and iterations executed grows to infinity. We also provide finite-sample results, which apply when a finite number of samples and iterations are作者: animated 時間: 2025-3-22 07:52
Learning and Using Modelshe types of models used in model-based methods and ways of learning them, as well as methods for planning on these models. In addition, we examine the typical architectures for combining model learning and planning, which vary depending on whether the designer wants the algorithm to run on-line, in 作者: 序曲 時間: 2025-3-22 10:01
Reinforcement Learning in Continuous State and Action Spacesblems and discuss many specific algorithms. Amongst others, we cover gradient-based temporal-difference learning, evolutionary strategies, policy-gradient algorithms and (natural) actor-critic methods. We discuss the advantages of different approaches and compare the performance of a state-of-the-ar作者: 樣式 時間: 2025-3-22 13:27
Predictively Defined Representations of Stateal system problem, it is particularly useful in a model-based RL context, when an agent must learn a representation of state and a model of system dynamics online: because the representation (and hence all of the model’s parameters) are defined using only statistics of observable quantities, their l作者: 擦試不掉 時間: 2025-3-22 18:19 作者: GRAZE 時間: 2025-3-22 23:48 作者: 使成整體 時間: 2025-3-23 02:45
wird. Darüber hinaus sind ihrer überzeugung nach Begabung und Pers?nlichkeit bedeutsam. Nach Darstellung der Studie und einer Interpretation der Ergebnisse werden abschlie?end Konsequenzen für eine nachhaltige Wirksamkeit des Praxissemesters mit dem Format des Forschenden Lernens diskutiert.作者: ambivalence 時間: 2025-3-23 08:58
genen Handlungssituationen, auf die Auseinandersetzung mit Unterrichtsbeobachtungen als Reflexionsfolie für eine theoretisch gestützte Diskussion professionellen Handelns sowie auf den ebenfalls theoriegestützten Entwurf von Handlungsalternativen. Gerahmt wird die eigenst?ndige forschungsbezogene Ak作者: maladorit 時間: 2025-3-23 12:23 作者: SHOCK 時間: 2025-3-23 17:22 作者: 搖擺 時間: 2025-3-23 20:31 作者: Interdict 時間: 2025-3-23 22:53
Lucian Bu?oniu,Alessandro Lazaric,Mohammad Ghavamzadeh,Rémi Munos,Robert Babu?ka,Bart De Schutterrd exemplarisch aufgezeigt, wie die Pluralisierung von Wissenschaft inhaltlich, methodisch und personell durch Forschendes Lernen vorangetrieben werden kann. Einblicke in das Programm ?e n t e r s c i e n c e“ veranschaulichen, inwiefern Forschendes Lernen als partizipativ angelegtes Lehr-Lern-Konze作者: Bumble 時間: 2025-3-24 06:20
Todd Hester,Peter Stonelichkeit und Literaturdidaktik. Darauf folgt ein überblick über m?gliche Formen des Forschenden Lernens, die sich in der literaturwissenschaftlichen Lehre realisieren lassen. Beispiele aus Literaturwissenschaft und Literaturdidaktik sollen dies illustrieren. Die Ausführungen schlie?en mit einer Disk作者: Thyroiditis 時間: 2025-3-24 07:22
Alessandro Lazaricrd exemplarisch aufgezeigt, wie die Pluralisierung von Wissenschaft inhaltlich, methodisch und personell durch Forschendes Lernen vorangetrieben werden kann. Einblicke in das Programm ?e n t e r s c i e n c e“ veranschaulichen, inwiefern Forschendes Lernen als partizipativ angelegtes Lehr-Lern-Konze作者: 小口啜飲 時間: 2025-3-24 12:25 作者: TRAWL 時間: 2025-3-24 16:33 作者: ligature 時間: 2025-3-24 21:45
me?baren Eigenschaften registriert hatten. Dafür gibt es ein fast legend?res Beispiel, das ich in Erinnerung rufen m?chte, n?mlich die berühmte Episode der Entdeckung des Neptun. Am Beginn des letzten Jahrhunderts hatten die Astronomen festgestellt, da? die Kreisbahn des Uranus nicht voll verstande作者: packet 時間: 2025-3-25 00:35 作者: jungle 時間: 2025-3-25 06:09
Matthijs T. J. Spaan me?baren Eigenschaften registriert hatten. Dafür gibt es ein fast legend?res Beispiel, das ich in Erinnerung rufen m?chte, n?mlich die berühmte Episode der Entdeckung des Neptun. Am Beginn des letzten Jahrhunderts hatten die Astronomen festgestellt, da? die Kreisbahn des Uranus nicht voll verstande作者: extinguish 時間: 2025-3-25 09:08 作者: VOC 時間: 2025-3-25 15:29
Ann Nowé,Peter Vrancx,Yann-Micha?l De Hauwere me?baren Eigenschaften registriert hatten. Dafür gibt es ein fast legend?res Beispiel, das ich in Erinnerung rufen m?chte, n?mlich die berühmte Episode der Entdeckung des Neptun. Am Beginn des letzten Jahrhunderts hatten die Astronomen festgestellt, da? die Kreisbahn des Uranus nicht voll verstande作者: 祖?zhèn)髫敭a(chǎn) 時間: 2025-3-25 18:21 作者: 培養(yǎng) 時間: 2025-3-25 23:58
Book 2012or finding optimal behaviors for challenging problems in control, optimization and adaptive behavior of intelligent agents. As a field, reinforcement learning has progressed tremendously in the past decade..The main goal of this book is to present an up-to-date series of survey articles on the main 作者: 不朽中國 時間: 2025-3-26 00:21 作者: 清晰 時間: 2025-3-26 05:59 作者: BRUNT 時間: 2025-3-26 09:14
Sample Complexity Bounds of Explorationo unify most existing model-based PAC-MDP algorithms for various subclasses of Markov decision processes.We also compare the sample-complexity framework to alternatives for formalizing exploration efficiency such as regret minimization and Bayes optimal solutions.作者: JECT 時間: 2025-3-26 15:24 作者: Infirm 時間: 2025-3-26 19:10
Evolutionary Computation for Reinforcement Learninging methods for evolving neural-network topologies and weights, hybrid methods that also use temporal-difference methods, coevolutionary methods for multi-agent settings, generative and developmental systems, and methods for on-line evolutionary reinforcement learning.作者: Synovial-Fluid 時間: 2025-3-26 22:45
Bayesian Reinforcement Learningally encoded in the prior distribution to speed up learning; b) the exploration/exploitation tradeoff can be naturally optimized; and c) notions of risk can be naturally taken into account to obtain robust policies.作者: chalice 時間: 2025-3-27 05:02 作者: 入伍儀式 時間: 2025-3-27 06:26 作者: Femine 時間: 2025-3-27 10:58
Reinforcement Learning and Markov Decision Processesaking problems in which there is limited feedback. This text introduces the intuitions and concepts behind Markov decision processes and two classes of algorithms for computing optimal behaviors: reinforcement learning and dynamic programming. First the formal framework of Markov decision process is作者: 玉米 時間: 2025-3-27 15:08
Batch Reinforcement Learningssible policy from a fixed set of a priori-known transition samples, the (batch) algorithms developed in this field can be easily adapted to the classical online case, where the agent interacts with the environment while learning. Due to the efficient use of collected data and the stability of the l作者: Resign 時間: 2025-3-27 19:19
Least-Squares Methods for Policy Iteration using function approximators to represent the solution. This chapter reviews least-squares methods for policy iteration, an important class of algorithms for approximate reinforcement learning. We discuss three techniques for solving the core, policy evaluation component of policy iteration, called作者: 愛好 時間: 2025-3-28 00:41
Learning and Using Modelsd functions of the domain on-line and plan a policy using this model. Once the method has learned an accurate model, it can plan an optimal policy on this model without any further experience in the world. Therefore, when model-based methods are able to learn a good model quickly, they frequently ha作者: 羽飾 時間: 2025-3-28 03:18
Transfer in Reinforcement Learning: A Framework and a Surveys to a target task. Whenever the tasks are ., the transferred knowledge can be used by a learning algorithm to solve the target task and significantly improve its performance (e.g., by reducing the number of samples needed to achieve a nearly optimal performance). In this chapter we provide a formal作者: Individual 時間: 2025-3-28 08:32
Sample Complexity Bounds of Exploration faster to near-optimal policies. While heuristics techniques are popular in practice, they lack formal guarantees and may not work well in general. This chapter studies algorithms with polynomial sample complexity of exploration, both model-based and model-free ones, in a unified manner. These so-c作者: Anal-Canal 時間: 2025-3-28 14:23
Reinforcement Learning in Continuous State and Action Spacese problems can been difficult, due to noise and delayed reinforcements. However, many real-world problems have continuous state or action spaces, which can make learning a good decision policy even more involved. In this chapter we discuss how to automatically find good decision policies in continuo作者: crescendo 時間: 2025-3-28 15:32 作者: 沉思的魚 時間: 2025-3-28 20:03
Hierarchical Approachestely and the results re-combined to find a solution to the original problem. It is well known that the na?ve application of reinforcement learning (RL) techniques fails to scale to more complex domains. This Chapter introduces hierarchical approaches to reinforcement learning that hold out the promi作者: Creatinine-Test 時間: 2025-3-28 23:25 作者: 組成 時間: 2025-3-29 03:44
Bayesian Reinforcement Learning prior distribution over unknown parameters and learning is achieved by computing a posterior distribution based on the data observed. Hence, Bayesian reinforcement learning distinguishes itself from other forms of reinforcement learning by explicitly maintaining a distribution over various quantiti作者: 用不完 時間: 2025-3-29 08:17
Partially Observable Markov Decision Processes have had many successes. In many problem domains, however, an agent suffers from limited sensing capabilities that preclude it from recovering a Markovian state signal from its perceptions. Extending the MDP framework, partially observable Markov decision processes (POMDPs) allow for principled dec作者: 思考 時間: 2025-3-29 12:39
Predictively Defined Representations of State important information from the past into some sort of state variable. In this chapter, we start with a broad examination of the concept of state, with emphasis on the fact that there are many possible representations of state for a given dynamical system, each with different theoretical and computa作者: Palate 時間: 2025-3-29 19:24 作者: delta-waves 時間: 2025-3-29 20:50
Decentralized POMDPsl reward based on local information only. This means that agents do not observe a Markovian signal during execution and therefore the agents’ individual policies map fromhistories to actions. Searching for an optimal joint policy is an extremely hard problem: it is NEXP-complete. This suggests, assu作者: reception 時間: 2025-3-30 00:39
Transfer in Reinforcement Learning: A Framework and a Survey improve its performance (e.g., by reducing the number of samples needed to achieve a nearly optimal performance). In this chapter we provide a formalization of the general transfer problem, we identify the main settings which have been investigated so far, and we review the most important approaches to transfer in reinforcement learning.作者: 閹割 時間: 2025-3-30 07:58
1867-4534 Reinforcement Learning.Includes a survey of previous papers.Reinforcement learning encompasses both a science of adaptive behavior of rational beings in uncertain environments and a computational methodology for finding optimal behaviors for challenging problems in control, optimization and adaptiv作者: 不規(guī)則的跳動 時間: 2025-3-30 09:13
trag zur professionellen Entwicklung angehender Lehrkr?fte. Die Vorstellung von Studierenden zur Relevanz einzelner Quellen dürfte die Nutzung von verschiedenen Lerngelegenheiten beeinflussen. Insofern setzt sich die Studie zum Ziel, herauszuarbeiten, welchen Stellenwert Sportstudierende mit dem Ber作者: 生氣的邊緣 時間: 2025-3-30 16:18
okus des Beitrags auf die F?rderung von Beobachtungskompetenz. Begründet wird dieser Schwerpunkt auf drei Ebenen: als Bestandteil einer grundlegenden wissenschaftlichen Kompetenz, als Basis für p?dagogisches Handeln in einem schwach strukturierten Berufsfeld und, angesichts biographischer Hintergrün
