Reinforcement Learning Using RBF Networks with Memory Mechanism

2003 ◽  
pp. 1149-1156
Author(s):  
Seiichi Ozawa ◽  
Naoto Shiraga
Keyword(s):  
Reinforcement Learning ◽  
Rbf Networks ◽  
Memory Mechanism ◽  
With Memory

scholarly journals Application of organic field-effect transistors in memory

2020 ◽  
Vol 4 (10) ◽  
pp. 2845-2862
Author(s):  
Zhiheng Zhu ◽  
Yunlong Guo ◽  
Yunqi Liu
Keyword(s):  
Field Effect ◽  
Field Effect Transistors ◽  
Memory Function ◽  
Organic Field Effect Transistors ◽  
Memory Mechanism ◽  
Memory Applications ◽  
With Memory

Functional organic field-effect transistors (OFETs) have developed rapidly, especially OFETs with memory function. We make a comprehensive summary of the background, memory mechanism, structure construction and memory applications based on OFETs.

scholarly journals Concept learning through deep reinforcement learning with memory-augmented neural networks

2019 ◽  
Vol 110 ◽  
pp. 47-54 ◽  
Author(s):  
Jing Shi ◽  
Jiaming Xu ◽  
Yiqun Yao ◽  
Bo Xu
Keyword(s):  
Neural Networks ◽  
Reinforcement Learning ◽  
Concept Learning ◽  
With Memory

scholarly journals The Missing Link Between Memory and Reinforcement Learning

2020 ◽  
Vol 11 ◽  
Author(s):  
Christian Balkenius ◽  
Trond A. Tjøstheim ◽  
Birger Johansson ◽  
Annika Wallin ◽  
Peter Gärdenfors
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Sensory Information ◽  
Sufficient Evidence ◽  
Extended Model ◽  
Missing Link ◽  
Memory Mechanism ◽  
State Action ◽  
Vicarious Trial And Error ◽  
Over Time

Reinforcement learning systems usually assume that a value function is defined over all states (or state-action pairs) that can immediately give the value of a particular state or action. These values are used by a selection mechanism to decide which action to take. In contrast, when humans and animals make decisions, they collect evidence for different alternatives over time and take action only when sufficient evidence has been accumulated. We have previously developed a model of memory processing that includes semantic, episodic and working memory in a comprehensive architecture. Here, we describe how this memory mechanism can support decision making when the alternatives cannot be evaluated based on immediate sensory information alone. Instead we first imagine, and then evaluate a possible future that will result from choosing one of the alternatives. Here we present an extended model that can be used as a model for decision making that depends on accumulating evidence over time, whether that information comes from the sequential attention to different sensory properties or from internal simulation of the consequences of making a particular choice. We show how the new model explains both simple immediate choices, choices that depend on multiple sensory factors and complicated selections between alternatives that require forward looking simulations based on episodic and semantic memory structures. In this framework, vicarious trial and error is explained as an internal simulation that accumulates evidence for a particular choice. We argue that a system like this forms the “missing link” between more traditional ideas of semantic and episodic memory, and the associative nature of reinforcement learning.

scholarly journals MemCap: Memorizing Style Knowledge for Image Captioning

2020 ◽  
Vol 34 (07) ◽  
pp. 12984-12992 ◽  
Author(s):  
Wentian Zhao ◽  
Xinxiao Wu ◽  
Xiaoxun Zhang
Keyword(s):  
Language Model ◽  
Attention Mechanism ◽  
Visual Content ◽  
Image Captioning ◽  
Memory Module ◽  
Training Corpus ◽  
Memory Mechanism ◽  
Linguistic Style ◽  
Related Part ◽  
With Memory

Generating stylized captions for images is a challenging task since it requires not only describing the content of the image accurately but also expressing the desired linguistic style appropriately. In this paper, we propose MemCap, a novel stylized image captioning method that explicitly encodes the knowledge about linguistic styles with memory mechanism. Rather than relying heavily on a language model to capture style factors in existing methods, our method resorts to memorizing stylized elements learned from training corpus. Particularly, we design a memory module that comprises a set of embedding vectors for encoding style-related phrases in training corpus. To acquire the style-related phrases, we develop a sentence decomposing algorithm that splits a stylized sentence into a style-related part that reflects the linguistic style and a content-related part that contains the visual content. When generating captions, our MemCap first extracts content-relevant style knowledge from the memory module via an attention mechanism and then incorporates the extracted knowledge into a language model. Extensive experiments on two stylized image captioning datasets (SentiCap and FlickrStyle10K) demonstrate the effectiveness of our method.

scholarly journals Interest Forwarding in Named Data Networking Using Reinforcement Learning

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3354 ◽  
Author(s):  
Olumide Akinwande
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Routing Protocol ◽  
Named Data Networking ◽  
Delivery Performance ◽  
Efficient Performance ◽  
Random Neural Network ◽  
Key Features ◽  
With Memory ◽  
Data Networking

In-network caching is one of the key features of information-centric networks (ICN), where forwarding entities in a network are equipped with memory with which they can temporarily store contents and satisfy en route requests. Exploiting in-network caching, therefore, presents the challenge of efficiently coordinating the forwarding of requests with the volatile cache states at the routers. In this paper, we address information-centric networks and consider in-network caching specifically for Named Data Networking (NDN) architectures. Our proposal departs from the forwarding algorithms which primarily use links that have been selected by the routing protocol for probing and forwarding. We propose a novel adaptive forwarding strategy using reinforcement learning with the random neural network (NDNFS-RLRNN), which leverages the routing information and actively seeks new delivery paths in a controlled way. Our simulations show that NDNFS-RLRNN achieves better delivery performance than a strategy that uses fixed paths from the routing layer and a more efficient performance than a strategy that retrieves contents from the nearest caches by flooding requests.

scholarly journals Spatial preferences account for inter-animal variability during the continual learning of a dynamic cognitive task

2019 ◽  
Author(s):  
David B. Kastner ◽  
Eric A. Miller ◽  
Zhounan Yang ◽  
Demetris K. Roumis ◽  
Daniel F. Liu ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Systematic Approach ◽  
Cognitive Task ◽  
Learning Ability ◽  
Spatial Alternation ◽  
Behavioral Design ◽  
Memory Support ◽  
With Memory ◽  
Alternation Task ◽  
Continual Learning

AbstractIndividual animals perform tasks in different ways, yet the nature and origin of that variability is poorly understood. In the context of spatial memory tasks, variability is often interpreted as resulting from differences in memory ability, but the validity of this interpretation is seldom tested since we lack a systematic approach for identifying and understanding factors that make one animal’s behavior different than another. Here we identify such factors in the context of spatial alternation in rats, a task often described as relying solely on memory of past choices. We combine hypothesis-driven behavioral design and reinforcement learning modeling to identify spatial preferences that, when combined with memory, support learning of a spatial alternation task. Identifying these preferences allows us to capture differences among animals, including differences in overall learning ability. Our results show that to understand the complexity of behavior requires quantitative accounts of the preferences of each animal.

Sign in / Sign up

Export Citation Format

Share Document