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Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control
2005
Nature Neuroscience
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Here, we consider dual-action choice systems from a normative perspective, using the computational theory of reinforcement learning. ...
We identify a key trade-off pitting computational simplicity against the flexible and statistically efficient use of experience. ...
By contrast, we suggest that the prefrontal circuit subserves a modelbased reinforcement learning method. ...
doi:10.1038/nn1560
pmid:16286932
fatcat:gleux5kgzvdwjk4jg44qzxlfoy
Evidence for long-term spatial memory in a parid
2011
Animal Cognition
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We speculate that this ability may potentially be useful in relocating caches if reinforced by repeated visits. ...
Future studies are necessary to test whether our results were speciWcally due to multiple reinforcements of the food-containing location and whether parids may have similar memory longevity during food-caching ...
This research was supervised by the University of Nevada, Reno, IACUC (protocol #A05/06-35) and followed all federal and local guidelines for the use of animals in research. ...
doi:10.1007/s10071-011-0440-3
pmid:21773746
fatcat:h453efttvrakzbd4gabcngvv7a
Page 1937 of Psychological Abstracts Vol. 84, Issue 5
[page]
1997
Psychological Abstracts
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Learning using faces of animals of another unfamiliar breed was also signifi- cantly better than for symbols but was significantly worse than that seen using faces of a familiar breed. ...
—Investigated the speed with which sheep learn to dis- criminate between familiar and unfamiliar faces, as opposed to symbols, to gain a food reward using a Y-maze apparatus. ...
Model and Machine Learning based Caching and Routing Algorithms for Cache-enabled Networks
[article]
2020
arXiv
pre-print
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We then discuss the applicability of multiple machine learning models, specifically reinforcement learning, deep learning, transfer learning and probabilistic graphical models for the caching and routing ...
In this paper, we compare and contrast model-based and machine learning approaches for designing caching and routing strategies to improve cache network performance (e.g., delay, hit rate). ...
We discuss the potential benefits of multiple different classes of machine learning algorithms, in particular reinforcement learning, deep learning, deep reinforcement learning, transfer learning, and ...
arXiv:2004.06787v1
fatcat:tp4f2xhpg5g63fomgmx5esiqmy
Evaluation of the hypothesis that phasic dopamine constitutes a cached-value signal
2018
Neurobiology of Learning and Memory
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reinforcement learning signal. ...
The phasic dopamine error signal is currently argued to be synonymous with the prediction error in Sutton and Barto (1987 Barto ( , 1998 model-free reinforcement learning algorithm (Schultz et al., 1997 ...
value learning, as described in model free reinforcement learning algorithms (Sutton & Barto, 1981 , 1987 . ...
doi:10.1016/j.nlm.2017.12.002
pmid:29269085
pmcid:PMC6136434
fatcat:btizu5lptzblpck3yucifwykmm
Interest Forwarding in Named Data Networking Using Reinforcement Learning
2018
Sensors
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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 ...
from the nearest caches by flooding requests. ...
NDN Forwarding with Reinforcement Learning Using the RNN We present NDNFS-RLRNN that uses reinforcement learning (RL) with the RNN for the strategy module per prefix in the NDN architecture, which operates ...
doi:10.3390/s18103354
fatcat:3c63dbozpvfinar3xd6xuzy36y
A DQN-Based Cache Strategy for Mobile Edge Networks
2022
Computers Materials & Continua
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deep reinforcement learning algorithm. ...
The deep neural network (DNN) and Q-learning algorithm are combined to design a deep reinforcement learning framework named as the deep-Q neural network (DQN), in which the DNN is adopted to represent ...
[27, 28] suggested a joint optimization for edge resource allocating problem, the proposed strategy uses the model-free actor-critic reinforcement learning to solve the joint optimization problems of ...
doi:10.32604/cmc.2022.020471
fatcat:5ht42r5pmvg3hggqrms5hoo5eu
Are animals stuck in time?
2002
Psychological bulletin
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A classic variable in early theories of learning was the delay of reinforcement. ...
Pavlov suggested that withholding the US (food) led to the growth of an inhibitory process that blocked the CR. ...
doi:10.1037/0033-2909.128.3.473
pmid:12002698
fatcat:3gz4z3oaarhp5gmg6kc24zz2om
Are animals stuck in time?
2002
Psychological bulletin
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A classic variable in early theories of learning was the delay of reinforcement. ...
Pavlov suggested that withholding the US (food) led to the growth of an inhibitory process that blocked the CR. ...
doi:10.1037//0033-2909.128.3.473
fatcat:bok3x4tdvzbzxmuwk5koxn4e4m
Deep Reinforcement Learning for Adaptive Caching in Hierarchical Content Delivery Networks
[article]
2019
arXiv
pre-print
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2019 pre-print arXiv:1902.10301v1 fatcat:3cn54dvmkzeyde4deu2vznawny
To model the two-way interactive influence between caching decisions at the parent and leaf nodes, a reinforcement learning framework is put forth. ...
To handle the large continuous state space, a scalable deep reinforcement learning approach is pursued. ...
This two-time scale caching task was cast in this paper using a reinforcement learning approach. ...
arXiv:1902.10301v2
fatcat:vqjy7jgolzdkjhufhmvc5mer6q
Enhanced dorsolateral striatal activity in drug use: The role of outcome in stimulus–response associations
2012
Behavioural Brain Research
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We describe how caching of reward value and Pavlovian to instrumental transfer can provide mechanisms for past and current reward values to regulate the performance of S-R habits. ...
This effect has been interpreted in light of evidence that this brain region supports the generation of habitual stimulus-response (S-R) based behaviors to propose the idea that prolonged drug use leads ...
These results suggest that the memory of the low value food pellets learned during exposure in the feeding cage was able to influence responding during the subsequent extinction test. ...
doi:10.1016/j.bbr.2012.07.042
pmid:22884607
pmcid:PMC3448372
fatcat:qreakrqcw5cb3dlovo5qgghkzq
Instrumental Learning
[chapter]
2017
Encyclopedia of Animal Cognition and Behavior
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Many broad mechanisms have been suggested including the use of olfaction, a biological compass, or the use of landmarks or beacons. ...
However, when a reinforcer from one system is used to reinforce a behavior from an incompatible system, learning is inhibited. ...
doi:10.1007/978-3-319-47829-6_1114-1
fatcat:r7zsh4zwsjeevf3yjbyx3dvruy
Q-Selector-Based Prefetching Method for DRAM/NVM Hybrid Main Memory System
2020
Electronics
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The Q-selector-based prefetching method is based on the Q-learning method, one of the reinforcement learning algorithms, which determines a near-optimal prefetcher for an application's current running ...
For this, our model analyzes real-time performance status to set the criteria for the Q-learning method. ...
The basic elements of reinforcement learning method are: In this paper, we suggest a DRAM-cache-based PCM hybrid main memory system with an intelligent prefetcher based on machine learning, which adopts ...
doi:10.3390/electronics9122158
fatcat:tsrauw6it5ayxjozoste4ovz6a
Page 4539 of Psychological Abstracts Vol. 79, Issue 11
[page]
1992
Psychological Abstracts
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—Describes a female chimpanzee that could learn the use of personal pronouns in a conditional matching-to-sample task. ...
Without putting their methodology or their results into question, analysis of the reinforcement conditions they used indicates that what was ac- tually examined was not transitive inference and that this ...
The impact of orbitofrontal dysfunction on cocaine addiction
2012
Nature Neuroscience
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This evidence suggests that cocaine-induced changes in orbitofrontal cortex disrupt the representation of states and transition functions that form the basis of flexible and adaptive 'model-based' behavioral ...
A set of reinforcement learning methods that use prediction errors to estimate and store scalar cue or action values from experience. ...
A set of reinforcement learning methods in which an internal model of the environment is learned and used to evaluate available actions or cues on the basis of their potential outcomes. ...
doi:10.1038/nn.3014
pmid:22267164
pmcid:PMC3701259
fatcat:orpo4vqfzjeinagliwsufmfnta
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