[PDF] Top 20 On the Sample Complexity of Reinforcement Learning
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On the Sample Complexity of Reinforcement Learning
... the sample complexity of ...“sample complexity of exploration” is 0 (N ^ A) (neglecting log and other relevant factors), which is the number of parameters required to specify the transition ... See full document
143
Spectral Learning of Latent-Variable PCFGs: Algorithms and Sample Complexity
... the learning algorithm of Bailly et ...the learning algorithm, in both a practical and theoretical ...of sample complexity, given in Theorem 8 of this paper, is much tighter than the ... See full document
51
Sample efficient Actor Critic Reinforcement Learning with Supervised Data for Dialogue Management
... early learning in the TRACER and eNACER algorithms as shown in Figure ...eNACER learning from a pre-trained SL model is reported ...eNACER learning from scratch, eNACER from SL model started with ... See full document
11
Learning Factor Graphs in Polynomial Time and Sample Complexity
... Recently, Narasimhan and Bilmes (2004) provided a polynomial time algorithm with a polyno- mial sample complexity guarantee for the class of Markov networks of bounded tree-width. Their algorithm computes ... See full document
46
Distribution-Dependent Sample Complexity of Large Margin Learning
... a sample to be shattered, as evident in Vapnik’s formulations of learnability as a function of the ε-entropy (Vapnik, ...a sample-complexity upper ...a sample drawn from a data distribution is ... See full document
31
Empirical Risk Minimization for Probabilistic Grammars: Sample Complexity and Hardness of Learning
... This article proceeds as follows. In Section 2 we review the background necessary from Vapnik’s (1988) empirical risk minimization framework. This framework is re- duced to maximum likelihood estimation when a specific ... See full document
48
Characterizing the Sample Complexity of Pure Private Learners
... The notion of probabilistic representation applies not only to private learning, but also to optimization problems. We consider a scenario where there is a domain X, a database S of m records, each taken from the ... See full document
33
Private Learning and Sanitization: Pure vs. Approximate Differential Privacy
... exhibiting sample complexity O(VC(C) log ...this sample complexity were given by [25, 3, ...private learning, we show significant differences between the sample complexity ... See full document
61
A Deep Reinforcement Learning Framework for Rebalancing Dockless Bike Sharing Systems
... ment learning algorithm called the hierarchical reinforce- ment pricing (HRP) ...hierarchical reinforcement learning framework (Dietterich ...the complexity issue due to high-dimensional in- ... See full document
8
Determinantal Reinforcement Learning
... multi-agent reinforcement learning, where the property of the RBM that allows effi- ciently sampling from a high dimensional space according to a Boltzmann distribution is ... See full document
8
Transfer Learning for Reinforcement Learning Domains: A Survey
... After learning one or more source tasks, some experience is gathered in the target task, which may have a different state space or transition ...batch learning algorithm then uses both source instances and ... See full document
53
Reducing the Time Complexity of Goal Independent Reinforcement Learning
... Concurrent Q-Learning (CQL) is a goal independent reinforcement learning technique that learns the action values to all states simultaneously. These action values may then be used in a similar way to ... See full document
6
The Sample Complexity of Learning Linear Predictors with the Squared Loss
... below). However, when we deal with the hypothesis class of norm-bounded predictors, then the excess risk can be larger by an arbitrary factor 1 . Therefore, upper bounds on these measures do not imply upper bounds on the ... See full document
12
The Sample Complexity of Dictionary Learning
... ies such? If the answer is affirmative, it implies that Theorem 11 is quite strong, and representation finding algorithms such as basis pursuit are almost always exact, which might help prove proper- ties of dictionary ... See full document
23
The Optimal Sample Complexity of PAC Learning
... the sample complexity of PAC learning is a long-standing open ...well-designed learning algorithms, and attempting to prove this has been the subject of much ...a sample ... See full document
15
Optimal Quantum Sample Complexity of Learning Algorithms
... to learning classical objects such as Boolean functions, one may also study the learnability of quantum ...PAC learning the error of the learner’s hypothesis is evaluated under the same distribution D that ... See full document
36
Learning Latent Tree Graphical Models
... Another popular class of reconstruction methods used in the phylogenetic community is the family of quartet-based distance methods (Bandelth and Dress, 1986; Erd˝os et al., 1999; Jiang et al., 2001). 3 Quartet-based ... See full document
42
Lifelong Reinforcement Learning On Mobile Robots
... To demonstrate our method on a real Turtlebot, four tasks were learned using conven- tional PG and then transfer was evaluated on the fifth task. The number of roll-outs is reduced to n = 11 and the number of ... See full document
197
Midbrain Dopamine Neurons Signal Belief in Choice Accuracy during a Perceptual Decision
... after learning, it contains the knowledge about the relationship between quality of internal evidence and the expected outcome of the decision, in other words, decision ... See full document
47
Experience based Reinforcement Learning to Acquire Effective Behavior in a Multiagent Domain
... the cut-loop routine is applied. Prot-sharing uses trial and error experiences, and reinforces eective rules instead of estimating values for the dierent state. Therefore, it uses this policy to escape states susceptible ... See full document
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