However , an alternative that can achieve the same goal is a feedback based approach in which the representation is formed in an iterative manner based on a feedback received from previous iterations output . We establish that a feedback based approach has several core advantages over feedforward : it enables making early predictions at the query time , its output naturally conforms to a hierarchical structure in the label space ( e.g. a taxonomy ) , and it provides a new basis for Curriculum Learning . We observe that feedback develops a considerably different representation compared to feedforward counterparts , in line with the aforementioned advantages . Yet , this interpretation of the educational technologist role shifts a great deal of power from educators to software companies , and not always to educational software companies . For training , schools increasingly rely on companies that apply a business model to education , and focus on software training rather than curriculum-based learning . And even in a university setting , the definition of an educational technologist changes . According to educational tools , which are commonly used in schools , the majority is made on a way , which does not promote moving but are beeing used in sitting position . In my thesis I will present didactic tool » Letter park « , which I developed for connecting curriculum based learning and movement . " Letter park " mainly focuses on the development of reading and writing . The problem faced by partners is the teacher 's inability to design curriculum learning 2013 by paying attention to students ' intelligence such as interpersonal , intrapersonal , visual , and kinesthetic . Students in a class have a variety of intelligence and learning styles . Teachers should accommodate all these intelligences in the learning process . It requires an activity on how to design 2013 curriculum learning planning . The method used in this training activity consists of three parts , namely the presentation of theories about designing curriculum learning 2013 . In this activity questionnaire sheets were used . More recently , several lines of research have explored how tasks , or data samples themselves , can be sequenced into a curriculum for the purpose of learning a problem that may otherwise be too difficult to learn from scratch . In this article , we present a framework for curriculum learning ( CL ) in reinforcement learning , and use it to survey and classify existing CL methods in terms of their assumptions , capabilities , and goals . Finally , we use our framework to find open problems and suggest directions for future RL curriculum learning research . Numerical methods for curriculum learning in the literature provides only initial heuristic solutions , with little to no guarantee on their quality . We define a new gray-box function that , including a suitable scheduling problem , can be effectively used to reformulate the curriculum learning problem . We propose different efficient numerical methods to address this gray-box reformulation . This work investigates a more challenging problem , e.g. , the newly-emerging semantic image inpainting - a task to fill in large holes in natural images . In this paper , we propose an end-to-end framework named progressive generative networks~(PGN ) , which regards the semantic image inpainting task as a curriculum learning problem . Specifically , we divide the hole filling process into several different phases and each phase aims to finish a course of the entire curriculum . In general , this activity aims to obtain information and data on the implementation of the national exam in order to improve its implementation as well as inputs for the improvement of curriculum and learning and other educational standards . The method used was a survey . Intelligent Learning Environments ( ILE ) are constructivist systems that attempt to incorporate beneficial aspects of tutoring systems and cognitive tools . ILEs support discovery learning through reflective interaction as well as curriculum-driven learning through scaffolding and coaching . ILEs are concerned with students developing both general and domain specific thinking and problem solving skills . This ABA approach is one of the ways that has been applied in the handling of grade IV elementary school students who experience selective mutism disorders with maximum results . Therefore , the ABA approach is recommended to be applied in the 2013 curriculum learning process specifically to help students who experience multifactorial disorders . In bilingual education , whether language is a key factor affecting curriculum learning situation has been concerned by the bilingual education circle , and how to answer that question determines the time and method of implementation of bilingual education . The research took minority bilingual students in primary and middle schools in Xinjiang as research objects , then investigated the learning and language situations of the research objects , next make sure the effect of language on their curriculum learning situation . The second main contribution of this paper is an effective meta - training curriculum . Curriculum learning and hard negative mining both suggest that faster convergence and stronger performance can be achieved by a better arrangement of training data . Inspired by these ideas , we design our hard task ( HT ) meta - batch strategy to offer a challenging but effective learning curriculum . Its superiority over Arc - I , however , is less salient when the sentences have deep grammatical structures and the matching relies less on the local matching patterns , as in Experiment - I. This therefore raises the interesting question about how to balance the representation of matching and the representations of objects , and whether we can guide the learning process through something like curriculum learning . As another important observation , convolutional models ( Arc - I & II , SENNA + MLP ) perform favorably over bag - of - words models , indicating the importance of utilizing sequential structures in understanding and matching sentences . The first one is FCNs in the Wild , which also adopted an adversarial training strategy , and further deployed a category specific adaptation method to align pixels of two domains that are likely from the same category . The second one is the curriculum learning adaptation , in which a curriculum learning approach was used to progressively adapt to the target domain . In both works , the dilation model with VGG - 16 In the present paper , we introduce the backdoor set approach into the field of temporal logic for the global fragment of linear temporal logic . We study the parameterized complexity of the satisfiability problem parameterized by the size of the backdoor . The approaches can be combined . Process algebras are not useful only for temporal logic verification : we remark the use of simulation/bisimulation for verification , for the hierarchical refinement design method , for the service redundancy analysis in a community , and for replacing a service with another one in a composition . The effective mechanisation of temporal logic is vital to the application of temporal reasoning in many fields , for example the verification of reactive systems , the implementation of temporal query languages , and temporal logic programming . Consequently , a variety of proof methods have been developed , implemented and applied . This paper explores a formalism for describing a wide class of multimedia document constraints . We outline the requirements on temporal logic specification that arise from the multimedia documents application area . In particular , we highlight a canonical document example . Based on time Petri nets , we put forward several linear firing rules among transitions . Using these rules and their combination , we can simplify the Petri net representation of complicated temporal relations , thus get the quantitative relations between time intervals and solve temporal logic problem within linear time complexity . So it is an effective method . We employ formal methods not to the design of the mobile application , but to characterise the different probabilistic patterns of actual use over various time cuts within a population of users . We define the whole process from identifying questions that give us insight into application usage , to event logging , data abstraction from logs , model inference , temporal logic property formulation , visualisation of results , and interpretation in the context of redesign . We illustrate the process through a real-life case study , which results in a new and principled way for selecting content for an extension to the mobile application . Reasoning in temporal logic has been used as an algorithmic approach for model checking for automatically verifying whether a hardware or software system functions correctly . In this paper , we present a computational method for direct computation of Groebner bases ( GB ) in Boolean rings for temporal logic reasoning and model checking . In contrast to other known algebraic approaches , the degree of intermediate polynomials during the calculation of Groebner bases using our method will never grow resulted in a significant improvement in running time and memory space consumption . We show that the logic system T(L ) preserves several properties of the original temporal logic like soundness , completeness , decidability , conservativeness and separation over linear flows of time . We then focus on the temporalisation of first-order logic , and a comparison is make with other first-order approaches to the handling of time . [ reference ] . Temporal reasoning helps in cases when image information is ambiguous due to close proximity of multiple people . For example the video - based approach succeeds in correctly localizing legs of the person in Fig . We also allow a forgetting factor in the updates to track non-stationary target functions ( i.e. for the use with optimistic policy iteration ) . Experimental comparison with standard CMAC function approximation indicate that LS-SVMs are well-suited for online RL . The power reduction is achieved by 1 . TL on meta-environments followed by online RL only on the last few layers of a deep convolutional neural network ( CNN ) instead of end-to-end ( E2E ) RL and 2 . Mapping of the algorithm onto a memory hierarchy where the pre-trained weights of all the conv layers and the first few fully connected ( FC ) layers are stored in dense , low standby leakage Spin Transfer Torque ( STT ) RAM eNVM arrays and the weights of the last few FC layers are stored in the on-die SRAM . As a result , existing RL benchmarks are not well-suited for the offline setting , making progress in this area difficult to measure . To design a benchmark tailored to offline RL , we start by outlining key properties of datasets relevant to applications of offline RL . Based on these properties , we design a set of benchmark tasks and datasets that evaluate offline RL algorithms under these conditions . Recently , the transformer architecture , which uses self-attention to model temporal context information , has been shown to achieve significantly lower word error rates ( WERs ) compared to recurrent neural network ( RNN ) based system architectures . Despite its success , the practical usage is limited to offline ASR tasks , since encoder-decoder architectures typically require an entire speech utterance as input . In this work , we propose a transformer based end-to-end ASR system for streaming ASR , where an output must be generated shortly after each spoken word . Although this procedure allows to achieve excellent recognition accuracy , it is far from being computationally efficient , in that it may produce a highly redundant signal ( i.e , almost identical spectral vectors may span many observation windows ) that converts into computational overload . The reduction of such overload can be very beneficial for application such as offline ASR on mobile devices . In this paper we present a principled way for saving numerical operations during ASR by using conditional-computation methods in deep bidirectional Recurrent Neural Networks ( RNNs ) for acoustic modelling . The results here present an optimistic view that robust RL algorithms trained on sufficiently large and diverse offline datasets can lead to high quality policies . The DQN replay dataset can serve as an offline RL benchmark and is open-sourced . This enables the pessimistic MDP to serve as a good surrogate for the purposes of policy evaluation and learning . Overall , MOReL is amenable to detailed theoretical analysis , enables easy and transparent design of practical algorithms , and leads to state-of-the-art results on widely studied offline RL benchmark tasks . Meta-learning can potentially help with both these issues by tuning hyperparameters online and allowing the algorithm to more robustly adjust to non-stationarity in a problem . This paper applies meta-gradient descent to derive a set of step-size tuning algorithms specifically for online RL control with eligibility traces . Our novel technique , Metatrace , makes use of an eligibility trace analogous to methods like Meta-learning can potentially help with both these issues by tuning hyperparameters online and allowing the algorithm to more robustly adjust to non-stationarity in a problem . This paper applies meta-gradient descent to derive a set of step-size tuning algorithms specifically for online RL control with eligibility traces . Our novel technique , Metatrace , makes use of an eligibility trace analogous to methods like Therefore , we compared the responses to both types of flickering at different frequency ranges , while also measuring the speed and accuracy of the BCI with short spelling tasks . The CMCD LED illumination yielded slightly superior performance in terms of offline ITR in comparison to the standard flickering . However , although many standard model-based RL methods already estimate the uncertainty of their model , they do not by themselves provide a mechanism to avoid the issues associated with distributional shift in the offline setting . We therefore propose to modify existing model-based RL methods to address these issues by casting offline model-based RL into a penalized MDP framework . We theoretically show that , by using this penalized MDP , we are maximizing a lower bound of the return in the true MDP . We found that larger perturbations led to better savings than smaller perturbations . In Experiment 2 , we studied how different features of the initial split perturbation influenced the generalization of learning . Interestingly , we found that practicing the same thing twice did not lead to fastest learning . Analogy and transfer Analogy and transfer of learning , which are two distinct areas in cognitive psychology as the lack of cross references between the two fields illustrates , are both concerned with the same basic questions : how can what has been learned in a specific situation be generalized to new situations and what conditions are necessary and/or sufficient to generate transfer from one situation to another ? We shall attempt to reanalyze both phenomena within the same theoretical framework and show how this analysis contributes to the fundamental question of the generalization of learning . Differences and similarities in the study of analogy and transfer . A Study in Large-Scale Reinforcement Learning . Large state and action spaces are very challenging to reinforcement learning . Data mining of the results used a novel method to capture curvilinear forgetting and spacing effects in the data and allowed a deeper analysis of the pedagogical implications of our task that revealed richer information than would have been revealed by the pretest-to-posttest comparison alone . Implications for musical education , generalization learning , and future research are discussed . In this letter , we have found a more general formulation of the REward Increment = Nonnegative Factor times Offset Reinforcement times Characteristic Eligibility ( REINFORCE ) learning principle first suggested by Williams . The new formulation has enabled us to apply the principle to global reinforcement learning in networks with various sources of randomness , and to suggest several simple local rules for such networks . Numerical simulations have shown that for simple classification and reinforcement learning tasks , at least one family of the new learning rules gives results comparable to those provided by the famous Rules Ar-i and Ar-p for the Boltzmann machines Reinforcement learning is one of the main adaptive mechanisms that is both well documented in animal behaviour and giving rise to computational studies in animats and robots . In this paper , we present TeXDYNA , an algorithm designed to solve large reinforcement learning problems with unknown structure by integrating hierarchical abstraction techniques of Hierarchical Reinforcement Learning and factorization techniques of Factored Reinforcement Learning . We validate our approach on the LIGHT BOX problem . The classifier tries to discriminate between samples drawn from the optimal state transition probability and those from the baseline one . Then , the estimated state value function is used to initialize the part of the deep neural networks for forward reinforcement learning . The proposed deep forward and inverse reinforcement learning is applied into two benchmark games : Atari 2600 and Reversi . Generalized learning is a kind of integrated machine learning methods , which combines symbolic learning with connective learning , fuzzy learning and evolution learning . This paper discusses the fundamental principle for generalized learning first . Then the generalized learning is introduced to remote sensing image recognition system ( RSIRS for short ) to obtain certain knowledge from training sample set for classification . We propose and evaluate several methods for three increasingly challenging variations of the task , including the method that won first place in the 2008 Reinforcement Learning Competition . The results demonstrate that ( 1 ) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering , ( 2 ) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models , ( 3 ) due to the difficulty of learning reliable models , model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and ( 4 ) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks . We propose a capsule network-based architecture for generalizing learning to new data with few examples . Using both generative and non-generative capsule networks with intermediate routing , we are able to generalize to new information over 25 times faster than a similar convolutional neural network . This makes proving AIXI 's optimality difficult . In fact , we prove that the current definition of AIXI can sometimes be suboptimal in a certain sense , but that this behavior is still the most rational one , hence emphasizing the difficulty of universal reinforcement learning . schaul11 also recently presented an interesting proposal for using games to measure the general capabilities of an agent . whiteson11 discuss a number of challenges in designing empirical tests to measure general reinforcement learning performance ; this work can be seen as attempting to address their important concerns . Starting in 2004 as a conference workshop , the Reinforcement Learning competition was held until 2009 ( a new iteration of the competition has been announced for 2013 ) .
However , an alternative that can achieve the same goal is a feedback based approach in which the representation is formed in an iterative manner based on a feedback received from previous iterations output . We establish that a feedback based approach has several core advantages over feedforward : it enables making early predictions at the query time , its output naturally conforms to a hierarchical structure in the label space ( e.g. a taxonomy ) , and it provides a new basis for Curriculum Learning . We observe that feedback develops a considerably different representation compared to feedforward counterparts , in line with the aforementioned advantages .
Yet , this interpretation of the educational technologist role shifts a great deal of power from educators to software companies , and not always to educational software companies . For training , schools increasingly rely on companies that apply a business model to education , and focus on software training rather than curriculum-based learning . And even in a university setting , the definition of an educational technologist changes .
According to educational tools , which are commonly used in schools , the majority is made on a way , which does not promote moving but are beeing used in sitting position . In my thesis I will present didactic tool » Letter park « , which I developed for connecting curriculum based learning and movement . " Letter park " mainly focuses on the development of reading and writing .
The problem faced by partners is the teacher 's inability to design curriculum learning 2013 by paying attention to students ' intelligence such as interpersonal , intrapersonal , visual , and kinesthetic . Students in a class have a variety of intelligence and learning styles . Teachers should accommodate all these intelligences in the learning process . It requires an activity on how to design 2013 curriculum learning planning . The method used in this training activity consists of three parts , namely the presentation of theories about designing curriculum learning 2013 . In this activity questionnaire sheets were used .
More recently , several lines of research have explored how tasks , or data samples themselves , can be sequenced into a curriculum for the purpose of learning a problem that may otherwise be too difficult to learn from scratch . In this article , we present a framework for curriculum learning ( CL ) in reinforcement learning , and use it to survey and classify existing CL methods in terms of their assumptions , capabilities , and goals . Finally , we use our framework to find open problems and suggest directions for future RL curriculum learning research .
Numerical methods for curriculum learning in the literature provides only initial heuristic solutions , with little to no guarantee on their quality . We define a new gray-box function that , including a suitable scheduling problem , can be effectively used to reformulate the curriculum learning problem . We propose different efficient numerical methods to address this gray-box reformulation .
This work investigates a more challenging problem , e.g. , the newly-emerging semantic image inpainting - a task to fill in large holes in natural images . In this paper , we propose an end-to-end framework named progressive generative networks~(PGN ) , which regards the semantic image inpainting task as a curriculum learning problem . Specifically , we divide the hole filling process into several different phases and each phase aims to finish a course of the entire curriculum .
In general , this activity aims to obtain information and data on the implementation of the national exam in order to improve its implementation as well as inputs for the improvement of curriculum and learning and other educational standards . The method used was a survey .
Intelligent Learning Environments ( ILE ) are constructivist systems that attempt to incorporate beneficial aspects of tutoring systems and cognitive tools . ILEs support discovery learning through reflective interaction as well as curriculum-driven learning through scaffolding and coaching . ILEs are concerned with students developing both general and domain specific thinking and problem solving skills .
This ABA approach is one of the ways that has been applied in the handling of grade IV elementary school students who experience selective mutism disorders with maximum results . Therefore , the ABA approach is recommended to be applied in the 2013 curriculum learning process specifically to help students who experience multifactorial disorders .
In bilingual education , whether language is a key factor affecting curriculum learning situation has been concerned by the bilingual education circle , and how to answer that question determines the time and method of implementation of bilingual education . The research took minority bilingual students in primary and middle schools in Xinjiang as research objects , then investigated the learning and language situations of the research objects , next make sure the effect of language on their curriculum learning situation .
The second main contribution of this paper is an effective meta - training curriculum . Curriculum learning and hard negative mining both suggest that faster convergence and stronger performance can be achieved by a better arrangement of training data . Inspired by these ideas , we design our hard task ( HT ) meta - batch strategy to offer a challenging but effective learning curriculum .
Its superiority over Arc - I , however , is less salient when the sentences have deep grammatical structures and the matching relies less on the local matching patterns , as in Experiment - I. This therefore raises the interesting question about how to balance the representation of matching and the representations of objects , and whether we can guide the learning process through something like curriculum learning . As another important observation , convolutional models ( Arc - I & II , SENNA + MLP ) perform favorably over bag - of - words models , indicating the importance of utilizing sequential structures in understanding and matching sentences .
The first one is FCNs in the Wild , which also adopted an adversarial training strategy , and further deployed a category specific adaptation method to align pixels of two domains that are likely from the same category . The second one is the curriculum learning adaptation , in which a curriculum learning approach was used to progressively adapt to the target domain . In both works , the dilation model with VGG - 16
In the present paper , we introduce the backdoor set approach into the field of temporal logic for the global fragment of linear temporal logic . We study the parameterized complexity of the satisfiability problem parameterized by the size of the backdoor .
The approaches can be combined . Process algebras are not useful only for temporal logic verification : we remark the use of simulation/bisimulation for verification , for the hierarchical refinement design method , for the service redundancy analysis in a community , and for replacing a service with another one in a composition .
The effective mechanisation of temporal logic is vital to the application of temporal reasoning in many fields , for example the verification of reactive systems , the implementation of temporal query languages , and temporal logic programming . Consequently , a variety of proof methods have been developed , implemented and applied .
This paper explores a formalism for describing a wide class of multimedia document constraints . We outline the requirements on temporal logic specification that arise from the multimedia documents application area . In particular , we highlight a canonical document example .
Based on time Petri nets , we put forward several linear firing rules among transitions . Using these rules and their combination , we can simplify the Petri net representation of complicated temporal relations , thus get the quantitative relations between time intervals and solve temporal logic problem within linear time complexity . So it is an effective method .
We employ formal methods not to the design of the mobile application , but to characterise the different probabilistic patterns of actual use over various time cuts within a population of users . We define the whole process from identifying questions that give us insight into application usage , to event logging , data abstraction from logs , model inference , temporal logic property formulation , visualisation of results , and interpretation in the context of redesign . We illustrate the process through a real-life case study , which results in a new and principled way for selecting content for an extension to the mobile application .
Reasoning in temporal logic has been used as an algorithmic approach for model checking for automatically verifying whether a hardware or software system functions correctly . In this paper , we present a computational method for direct computation of Groebner bases ( GB ) in Boolean rings for temporal logic reasoning and model checking . In contrast to other known algebraic approaches , the degree of intermediate polynomials during the calculation of Groebner bases using our method will never grow resulted in a significant improvement in running time and memory space consumption .
We show that the logic system T(L ) preserves several properties of the original temporal logic like soundness , completeness , decidability , conservativeness and separation over linear flows of time . We then focus on the temporalisation of first-order logic , and a comparison is make with other first-order approaches to the handling of time .
[ reference ] . Temporal reasoning helps in cases when image information is ambiguous due to close proximity of multiple people . For example the video - based approach succeeds in correctly localizing legs of the person in Fig .
We also allow a forgetting factor in the updates to track non-stationary target functions ( i.e. for the use with optimistic policy iteration ) . Experimental comparison with standard CMAC function approximation indicate that LS-SVMs are well-suited for online RL .
The power reduction is achieved by 1 . TL on meta-environments followed by online RL only on the last few layers of a deep convolutional neural network ( CNN ) instead of end-to-end ( E2E ) RL and 2 . Mapping of the algorithm onto a memory hierarchy where the pre-trained weights of all the conv layers and the first few fully connected ( FC ) layers are stored in dense , low standby leakage Spin Transfer Torque ( STT ) RAM eNVM arrays and the weights of the last few FC layers are stored in the on-die SRAM .
As a result , existing RL benchmarks are not well-suited for the offline setting , making progress in this area difficult to measure . To design a benchmark tailored to offline RL , we start by outlining key properties of datasets relevant to applications of offline RL . Based on these properties , we design a set of benchmark tasks and datasets that evaluate offline RL algorithms under these conditions .
Recently , the transformer architecture , which uses self-attention to model temporal context information , has been shown to achieve significantly lower word error rates ( WERs ) compared to recurrent neural network ( RNN ) based system architectures . Despite its success , the practical usage is limited to offline ASR tasks , since encoder-decoder architectures typically require an entire speech utterance as input . In this work , we propose a transformer based end-to-end ASR system for streaming ASR , where an output must be generated shortly after each spoken word .
Although this procedure allows to achieve excellent recognition accuracy , it is far from being computationally efficient , in that it may produce a highly redundant signal ( i.e , almost identical spectral vectors may span many observation windows ) that converts into computational overload . The reduction of such overload can be very beneficial for application such as offline ASR on mobile devices . In this paper we present a principled way for saving numerical operations during ASR by using conditional-computation methods in deep bidirectional Recurrent Neural Networks ( RNNs ) for acoustic modelling .
The results here present an optimistic view that robust RL algorithms trained on sufficiently large and diverse offline datasets can lead to high quality policies . The DQN replay dataset can serve as an offline RL benchmark and is open-sourced .
This enables the pessimistic MDP to serve as a good surrogate for the purposes of policy evaluation and learning . Overall , MOReL is amenable to detailed theoretical analysis , enables easy and transparent design of practical algorithms , and leads to state-of-the-art results on widely studied offline RL benchmark tasks .
Meta-learning can potentially help with both these issues by tuning hyperparameters online and allowing the algorithm to more robustly adjust to non-stationarity in a problem . This paper applies meta-gradient descent to derive a set of step-size tuning algorithms specifically for online RL control with eligibility traces . Our novel technique , Metatrace , makes use of an eligibility trace analogous to methods like
Meta-learning can potentially help with both these issues by tuning hyperparameters online and allowing the algorithm to more robustly adjust to non-stationarity in a problem . This paper applies meta-gradient descent to derive a set of step-size tuning algorithms specifically for online RL control with eligibility traces . Our novel technique , Metatrace , makes use of an eligibility trace analogous to methods like
Therefore , we compared the responses to both types of flickering at different frequency ranges , while also measuring the speed and accuracy of the BCI with short spelling tasks . The CMCD LED illumination yielded slightly superior performance in terms of offline ITR in comparison to the standard flickering .
However , although many standard model-based RL methods already estimate the uncertainty of their model , they do not by themselves provide a mechanism to avoid the issues associated with distributional shift in the offline setting . We therefore propose to modify existing model-based RL methods to address these issues by casting offline model-based RL into a penalized MDP framework . We theoretically show that , by using this penalized MDP , we are maximizing a lower bound of the return in the true MDP .
We found that larger perturbations led to better savings than smaller perturbations . In Experiment 2 , we studied how different features of the initial split perturbation influenced the generalization of learning . Interestingly , we found that practicing the same thing twice did not lead to fastest learning .
Analogy and transfer Analogy and transfer of learning , which are two distinct areas in cognitive psychology as the lack of cross references between the two fields illustrates , are both concerned with the same basic questions : how can what has been learned in a specific situation be generalized to new situations and what conditions are necessary and/or sufficient to generate transfer from one situation to another ? We shall attempt to reanalyze both phenomena within the same theoretical framework and show how this analysis contributes to the fundamental question of the generalization of learning . Differences and similarities in the study of analogy and transfer .
A Study in Large-Scale Reinforcement Learning . Large state and action spaces are very challenging to reinforcement learning .
Data mining of the results used a novel method to capture curvilinear forgetting and spacing effects in the data and allowed a deeper analysis of the pedagogical implications of our task that revealed richer information than would have been revealed by the pretest-to-posttest comparison alone . Implications for musical education , generalization learning , and future research are discussed .
In this letter , we have found a more general formulation of the REward Increment = Nonnegative Factor times Offset Reinforcement times Characteristic Eligibility ( REINFORCE ) learning principle first suggested by Williams . The new formulation has enabled us to apply the principle to global reinforcement learning in networks with various sources of randomness , and to suggest several simple local rules for such networks . Numerical simulations have shown that for simple classification and reinforcement learning tasks , at least one family of the new learning rules gives results comparable to those provided by the famous Rules Ar-i and Ar-p for the Boltzmann machines
Reinforcement learning is one of the main adaptive mechanisms that is both well documented in animal behaviour and giving rise to computational studies in animats and robots . In this paper , we present TeXDYNA , an algorithm designed to solve large reinforcement learning problems with unknown structure by integrating hierarchical abstraction techniques of Hierarchical Reinforcement Learning and factorization techniques of Factored Reinforcement Learning . We validate our approach on the LIGHT BOX problem .
The classifier tries to discriminate between samples drawn from the optimal state transition probability and those from the baseline one . Then , the estimated state value function is used to initialize the part of the deep neural networks for forward reinforcement learning . The proposed deep forward and inverse reinforcement learning is applied into two benchmark games : Atari 2600 and Reversi .
Generalized learning is a kind of integrated machine learning methods , which combines symbolic learning with connective learning , fuzzy learning and evolution learning . This paper discusses the fundamental principle for generalized learning first . Then the generalized learning is introduced to remote sensing image recognition system ( RSIRS for short ) to obtain certain knowledge from training sample set for classification .
We propose and evaluate several methods for three increasingly challenging variations of the task , including the method that won first place in the 2008 Reinforcement Learning Competition . The results demonstrate that ( 1 ) neuroevolution can be effective for complex on-line reinforcement learning tasks such as generalized helicopter hovering , ( 2 ) neuroevolution excels at finding effective helicopter hovering policies but not at learning helicopter models , ( 3 ) due to the difficulty of learning reliable models , model-based approaches to helicopter hovering are feasible only when domain expertise is available to aid the design of a suitable model representation and ( 4 ) recent advances in efficient resampling can enable neuroevolution to tackle more aggressively generalized reinforcement learning tasks .
We propose a capsule network-based architecture for generalizing learning to new data with few examples . Using both generative and non-generative capsule networks with intermediate routing , we are able to generalize to new information over 25 times faster than a similar convolutional neural network .
This makes proving AIXI 's optimality difficult . In fact , we prove that the current definition of AIXI can sometimes be suboptimal in a certain sense , but that this behavior is still the most rational one , hence emphasizing the difficulty of universal reinforcement learning .
schaul11 also recently presented an interesting proposal for using games to measure the general capabilities of an agent . whiteson11 discuss a number of challenges in designing empirical tests to measure general reinforcement learning performance ; this work can be seen as attempting to address their important concerns . Starting in 2004 as a conference workshop , the Reinforcement Learning competition was held until 2009 ( a new iteration of the competition has been announced for 2013 ) .
Feedback Networks The Rise and Fall of Educational Technology: Did We Miss the Point? We Need Educational Technologists Who Are Developers of Learning Tools, Not Just Software-Installing Computer Practitioners Development of language skills with the help of didactic tool 'Letter park' in the first grade of primary school The Training on saintific learning design with optimizing intelligence and student learning style in Ar-Royan Natural School Padang Curriculum Learning for Reinforcement Learning Domains: A Framework and Survey A Gray-Box Approach for Curriculum Learning Semantic Image Inpainting with Progressive Generative Networks The Evaluation of Implementation the National Exam Input as a Learning Improvement AdventurePlayer: an intelligent learning environment Applied Behavior Analysis Approach In The 2013 Curriculum Investigation on the Relationship between Curriculum Learning Situation and Language of Bilingual Students Meta-Transfer Learning for Few-Shot Learning Convolutional Neural Network Architectures for Matching Natural Language Sentences ROAD: Reality Oriented Adaptation for Semantic Segmentation of Urban Scenes Backdoors for Linear Temporal Logic Web services: a process algebra approach Guiding Clausal Temporal Resolution Specification and Prototyping of Structured Multimedia Documents using Interval Temporal Logic Extended Interval Temporal Logic for Undetermined Interval:Modeling and Linear Inference Using Time Petri Nets Probabilistic Formal Analysis of App Usage to Inform Redesign A Computational Method for Temporal Logic Reasoning and Model Checking Adding a temporal dimension to a logic system ArtTrack: Articulated Multi-Person Tracking in the Wild Least Squares SVM for Least Squares TD Learning Hierarchical Memory System With STT-MRAM and SRAM to Support Transfer and Real-Time Reinforcement Learning in Autonomous Drones Datasets for Data-Driven Reinforcement Learning Streaming Automatic Speech Recognition with the Transformer Model Conditional-Computation-Based Recurrent Neural Networks for Computationally Efficient Acoustic Modelling An Optimistic Perspective on Offline Reinforcement Learning MOReL : Model-Based Offline Reinforcement Learning Metatrace Actor-Critic: Online Step-Size Tuning by Meta-gradient Descent for Reinforcement Learning Control Metatrace: Online Step-size Tuning by Meta-gradient Descent for Reinforcement Learning Control Custom-Made Monitor for Easy High-Frequency SSVEP Stimulation MOPO: Model-based Offline Policy Optimization Creating flexible motor memories in human walking Analogy and Transfer: Encoding the Problem at the Right Level of Abstraction Algorithms or Actions? A Study in Large-Scale Reinforcement Learning Modeling and Optimizing Forgetting and Spacing Effects during Musical Interval Training Global Reinforcement Learning in Neural Networks TeXDYNA: Hierarchical Reinforcement Learning in Factored MDPs Model-Free Deep Inverse Reinforcement Learning by Logistic Regression The applications of generalized learning in RSIRS Neuroevolutionary reinforcement learning for generalized control of simulated helicopters Capsule networks for low-data transfer learning Asymptotic non-learnability of universal agents with computable horizon functions The Arcade Learning Environment: An Evaluation Platform for General Agents (Extended Abstract)
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