scholarly journals Valence biases in reinforcement learning shift across adolescence and modulate subsequent memory

2020 ◽  
Author(s):  
Gail Rosenbaum ◽  
Hannah Grassie ◽  
Catherine A. Hartley
Keyword(s):  
Reinforcement Learning ◽  
Trial And Error ◽  
Independent Dataset ◽  
Age Related ◽  
Subjective Value ◽  
Valuation Process ◽  
Incidental Memory ◽  
Past Experiences ◽  
Expected Outcomes ◽  
Better Than

Individuals learn differently through trial and error, with some more influenced by good outcomes, and others weighting bad outcomes more heavily. Such valence biases may also influence memory for past experiences. Here, we examined whether valence asymmetries in reinforcement learning change across adolescence, and whether individual learning asymmetries bias the content of subsequent memory. Participants ages 8-27 learned the values of “point machines”, after which their memory for trial-unique images presented with choice outcomes was assessed. Relative to children and adults, adolescents overweighted worse-than-expected outcomes during learning. Individuals’ valence biases modulated incidental memory, such that those who prioritized worse- (or better-) than-expected outcomes during learning were also more likely to remember images paired with these outcomes, an effect reproduced in an independent dataset. Collectively, these results highlight age-related changes in the computation of subjective value, and demonstrate that a valence-asymmetric valuation process influences how information is prioritized in episodic memory.

scholarly journals Flexibility in valenced reinforcement learning computations across development

2021 ◽  
Author(s):  
Kate Nussenbaum ◽  
Juan A. Velez ◽  
Bradli T. Washington ◽  
Hannah E. Hamling ◽  
Catherine A. Hartley
Keyword(s):  
Reinforcement Learning ◽  
Mixed Race ◽  
Positive Outcomes ◽  
Negative Outcomes ◽  
Optimal Integration ◽  
Adolescents And Adults ◽  
Context Dependent ◽  
Expected Outcomes ◽  
Better Than

Optimal integration of positive and negative outcomes during learning varies depending on an environment’s reward statistics. The present study investigated the extent to which children, adolescents, and adults (N = 142 8 - 25 year-olds, 55% female, 42% White, 31% Asian, 17% mixed race, and 8% Black) adapt their weighting of better-than-expected and worse-than-expected outcomes when learning from reinforcement. Participants made a series of choices across two contexts: one in which weighting positive outcomes more heavily than negative outcomes led to better performance, and one in which the reverse was true. Reinforcement learning modeling revealed that across age, participants shifted their valence biases in accordance with the structure of the environment. Exploratory analyses revealed increases in context-dependent flexibility with age.

scholarly journals Reinforcement learning versus swarm intelligence for autonomous multi-HAPS coordination

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
Ogbonnaya Anicho ◽  
Philip B. Charlesworth ◽  
Gurvinder S. Baicher ◽  
Atulya K. Nagar
Keyword(s):  
Reinforcement Learning ◽  
State Space ◽  
Swarm Intelligence ◽  
Performance Indicators ◽  
Convergence Rates ◽  
Tuning Parameters ◽  
Continuous State Space ◽  
Continuous State ◽  
User Coverage ◽  
Better Than

AbstractThis work analyses the performance of Reinforcement Learning (RL) versus Swarm Intelligence (SI) for coordinating multiple unmanned High Altitude Platform Stations (HAPS) for communications area coverage. It builds upon previous work which looked at various elements of both algorithms. The main aim of this paper is to address the continuous state-space challenge within this work by using partitioning to manage the high dimensionality problem. This enabled comparing the performance of the classical cases of both RL and SI establishing a baseline for future comparisons of improved versions. From previous work, SI was observed to perform better across various key performance indicators. However, after tuning parameters and empirically choosing suitable partitioning ratio for the RL state space, it was observed that the SI algorithm still maintained superior coordination capability by achieving higher mean overall user coverage (about 20% better than the RL algorithm), in addition to faster convergence rates. Though the RL technique showed better average peak user coverage, the unpredictable coverage dip was a key weakness, making SI a more suitable algorithm within the context of this work.

scholarly journals Age Prediction of Human Based on DNA Methylation by Blood Tissues

Genes ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 870
Author(s):  
Jiansheng Zhang ◽  
Hongli Fu ◽  
Yan Xu
Keyword(s):  
Dna Methylation ◽  
Pearson Correlation ◽  
Close Correlation ◽  
Gradient Boosting ◽  
Tissue Samples ◽  
Regression Methods ◽  
Cpg Sites ◽  
Testing Dataset ◽  
Age Related ◽  
Better Than

In recent years, scientists have found a close correlation between DNA methylation and aging in epigenetics. With the in-depth research in the field of DNA methylation, researchers have established a quantitative statistical relationship to predict the individual ages. This work used human blood tissue samples to study the association between age and DNA methylation. We built two predictors based on healthy and disease data, respectively. For the health data, we retrieved a total of 1191 samples from four previous reports. By calculating the Pearson correlation coefficient between age and DNA methylation values, 111 age-related CpG sites were selected. Gradient boosting regression was utilized to build the predictive model and obtained the R2 value of 0.86 and MAD of 3.90 years on testing dataset, which were better than other four regression methods as well as Horvath’s results. For the disease data, 354 rheumatoid arthritis samples were retrieved from a previous study. Then, 45 CpG sites were selected to build the predictor and the corresponded MAD and R2 were 3.11 years and 0.89 on the testing dataset respectively, which showed the robustness of our predictor. Our results were better than the ones from other four regression methods. Finally, we also analyzed the twenty-four common CpG sites in both healthy and disease datasets which illustrated the functional relevance of the selected CpG sites.

Synthesis and Evaluation of bis-Schiff Bases of Carbohydrazide as Antioxidant and Cytotoxic Agents

2021 ◽  
Vol 18 ◽  
Author(s):  
Sarosh Iqbal ◽  
Shumaila Kiran ◽  
Shahida Perveen ◽  
Rizwana Malik ◽  
Muhammad Taha ◽  
...  
Keyword(s):  
Schiff Bases ◽  
Radical Scavenging Activity ◽  
Radical Scavenging ◽  
Cytotoxic Agents ◽  
Scavenging Activity ◽  
Standard Drug ◽  
Age Related ◽  
Dpph Radical Scavenging ◽  
Better Than

Background & Introduction : Antioxidants are known to prevent oxidative stress-induced damage to the biomolecules and thus, delay the onset of cancers and many age-related diseases. Therefore, the development of novel and potent antioxidants is justified. Method: During this study, we synthesized symmetrical bis-Schiff bases of carbohydrazide 1-27, and evaluated their in vitro antioxidative activity and cytotoxic activity. Results: Among synthesized compounds, six compounds 20 (IC50 = 12.89 ± 0.02 µM), 16 (IC50 = 14.32 ± 0.43 µM), 17 (IC50 = 18.52 ± 0.83 µM), 19 (IC50 = 22.84 ± 0.62 µM), 24 (IC50 = 35.1 ± 0.82 µM) and 15 (IC50 = 40.03 ± 1.06 µM) showed an excellent 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity, better than the standard butylatedhydroxyanisole (BHA) (IC50 = 44.6 ± 0.6 µM). Likewise, two compounds 16 (IC50 = 4.3 ± 1.3 µM) and 20 (IC50 = 6.6 ± 1.6 µM) showed oxidative burst scavenging activity better than the standard drug ibuprofen (IC50 = 11.2 ± 1.9 µM). Some synthesized compounds showed good to moderate toxicity against prostate cancer (PC-3) cell lines. Conclusion: This study has identified potent antioxidants and good cytotoxic agents with the potential to further investigate.

scholarly journals Differences in the Motor Coordination Abilities Among Adolescent Gymnasts, Swimmers, and Ice Hockey Players

2017 ◽  
Vol 18 (1) ◽  
Author(s):  
Timo Jaakkola ◽  
Anthony Watt ◽  
Sami Kalaja
Keyword(s):  
Motor Coordination ◽  
Ice Hockey ◽  
Specific Training ◽  
Young Athletes ◽  
Test Protocol ◽  
Male Adolescents ◽  
Age Related ◽  
Study Results ◽  
Hockey Players ◽  
Better Than

AbstractPurpose. Motor coordination is proposed to be a relatively stable age-related construct, unlikely to be influenced by aligned experiential factors such as intensive sport-specific training. The purpose of the study is to investigate if there are differences in motor coordination abilities among young artistic gymnasts, swimmers, and ice hockey players.Methods. The participants of the study were 508 female and 258 male adolescents (age, M = 12.80, SD = 1.10) comprising artistic gymnasts (n = 463), swimmers (n = 70), and ice hockey players (n = 233). The KTK-test protocol was used to analyse their gross motor coordination abilities.Results. The results of the study demonstrated that gymnasts scored better than ice hockey players and swimmers in the test of walking backwards along a beam, and better than ice hockey players in total motor coordination, hopping over an obstacle, and the test of moving sideways on wooden boards. However, ice hockey players scored higher than swimmers and gymnasts in the test of jumping from side to side. Subsequently, swimmers obtained better results in the test of moving sideways on wooden boards as compared with ice hockey players.Conclusions. The study results indicate that intensive sport-specific training may extend young athletes′ motor coordination characteristics in the ability areas representative of the sport in which they engage.

scholarly journals REINFORCEMENT LEARNING BASED ANTI-COLLISION ALGORITHM FOR RFID SYSTEMS

2019 ◽  
pp. 155-168
Author(s):  
Murukesan Loganathan ◽  
Thennarasan Sabapathy ◽  
Mohamed Elobaid Elshaikh ◽  
Mohamed Nasrun Osman ◽  
Rosemizi Abd Rahim ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Radio Frequency Identification ◽  
Energy Efficient ◽  
Current Standard ◽  
Tag Identification ◽  
Rfid Systems ◽  
Order Of Magnitude ◽  
Control Message ◽  
Frequency Identification ◽  
Better Than

Efficient collision arbitration protocol facilitates fast tag identification in radio frequency identification (RFID) systems. EPCGlobal-Class1-Generation2 (EPC-C1G2) protocol is the current standard for collision arbitration in commercial RFID systems. However, the main drawback of this protocol is that it requires excessive message exchanges between tags and the reader for its operation. This wastes energy of the already resource-constrained RFID readers. Hence, in this work, reinforcement learning based anti-collision protocol (RL-DFSA) is proposed to address the energy efficient collision arbitration problem in the RFID system. The proposed algorithm continuously learns and adapts to the changes in the environment by devising an optimal policy. The proposed RL-DFSA was evaluated through extensive simulations and compared with the variants of EPC-C1G2 algorithms that are currently being used in the commercial readers. Based on the results, it is concluded that RL-DFSA performs equal or better than EPC-C1G2 protocol in delay, throughput and time system efficiency when simulated for sparse and dense environments while requiring one order of magnitude lesser control message exchanges between the reader and the tags.

scholarly journals Visual Outcomes of Macula-Involving Rhegmatogenous Retinal Detachment in Patients with and Without Age-Related Macular Degeneration

2022 ◽  
Author(s):  
P. Barrett Paulk ◽  
Dala Eloubeidi ◽  
John O. Mason III ◽  
Christine A. Curcio ◽  
Jason N. Crosson ◽  
...  
Keyword(s):  
Visual Acuity ◽  
Retinal Detachment ◽  
Macular Degeneration ◽  
Rhegmatogenous Retinal Detachment ◽  
Age Related Macular Degeneration ◽  
Light Perception ◽  
Postoperative Visual Acuity ◽  
Age Related ◽  
Functional Vision ◽  
Better Than

Abstract Background Patients presenting with macula-off rhegmatogenous retinal detachment (RRD) with concomitant age-related macular degeneration (AMD) and their treating physicians would benefit from knowledge regarding the visual prognosis after repair. The prognosis for such patients is not well known. The purpose of this study is to compare visual outcomes in macula-off RRD in eyes with AMD versus a group of comparison eyes without AMD. Methods This was a retrospective chart review of 1,149 patients. A total of 191 eyes met study criteria, 162 non-AMD eyes (controls) and 29 AMD eyes. The main outcome measure was postoperative visual acuity in control eyes versus AMD eyes, and this was compared using Fisher’s exact test. Results There was a statistically significant difference in postoperative visual acuity by AMD status, with those without AMD having a higher frequency of Count Fingers (CF), Hand Motion (HM), Light Perception (LP), or No Light Perception (NLP) vision (p = 0.023). More specifically 5.56% of non-AMD eyes and 3.45% of AMD eyes were 20/40 or better, 77.16% of non-AMD and 55.17% of AMD eyes were worse than 20/40 and better than 20/200, 10.49% of non-AMD eyes and 37.93% of AMD eyes were 20/200 or worse, and there were 11 eyes in the non-AMD group with CF, HM, LP, or NLP vision while there was only 1 eye in the AMD group with CF vision. Conclusions Though postoperative visual acuity was worse in the non-AMD group with a higher frequency of patients having final vision of CF, HM, LP, or NLP, this is not likely a clinically significant finding. Rather, it is a function of the difference in sample size and composition between the two groups. Importantly, this study suggests AMD patients can expect similar outcomes to non-AMD patients after RRD repair. Our study suggests that approximately 58% of patients with AMD can expect to maintain functional vision better than 20/200. We conclude that AMD patients can achieve functional vision after RRD surgery, similar to those without AMD. These findings may be helpful in guiding realistic expectations of AMD patients with RRD.

scholarly journals Methods and Algorithms for Knowledge Reuse in Multiagent Reinforcement Learning

2020 ◽  
Author(s):  
Felipe Leno Da Silva ◽  
Anna Helena Reali Costa
Keyword(s):  
Reinforcement Learning ◽  
Transfer Learning ◽  
Learning Process ◽  
Trial And Error ◽  
Knowledge Reuse ◽  
Previous Knowledge ◽  
Learning Methods ◽  
Types Of Knowledge ◽  
Learning Agent ◽  
Multiagent Reinforcement Learning

Reinforcement Learning (RL) is a powerful tool that has been used to solve increasingly complex tasks. RL operates through repeated interactions of the learning agent with the environment, via trial and error. However, this learning process is extremely slow, requiring many interactions. In this thesis, we leverage previous knowledge so as to accelerate learning in multiagent RL problems. We propose knowledge reuse both from previous tasks and from other agents. Several flexible methods are introduced so that each of these two types of knowledge reuse is possible. This thesis adds important steps towards more flexible and broadly applicable multiagent transfer learning methods.

scholarly journals Pemanfaatan Asynchronous Advantage Actor-Critic Dalam Pembuatan AI Game Bot Pada Game Arcade

2019 ◽  
Vol 1 (2) ◽  
pp. 74-84
Author(s):  
Evan Kusuma Susanto ◽  
Yosi Kristian
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Reinforcement Learning ◽  
Convolutional Neural Network ◽  
Short Term Memory ◽  
Trial And Error ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory

Asynchronous Advantage Actor-Critic (A3C) adalah sebuah algoritma deep reinforcement learning yang dikembangkan oleh Google DeepMind. Algoritma ini dapat digunakan untuk menciptakan sebuah arsitektur artificial intelligence yang dapat menguasai berbagai jenis game yang berbeda melalui trial and error dengan mempelajari tempilan layar game dan skor yang diperoleh dari hasil tindakannya tanpa campur tangan manusia. Sebuah network A3C terdiri dari Convolutional Neural Network (CNN) di bagian depan, Long Short-Term Memory Network (LSTM) di tengah, dan sebuah Actor-Critic network di bagian belakang. CNN berguna sebagai perangkum dari citra output layar dengan mengekstrak fitur-fitur yang penting yang terdapat pada layar. LSTM berguna sebagai pengingat keadaan game sebelumnya. Actor-Critic Network berguna untuk menentukan tindakan terbaik untuk dilakukan ketika dihadapkan dengan suatu kondisi tertentu. Dari hasil percobaan yang dilakukan, metode ini cukup efektif dan dapat mengalahkan pemain pemula dalam memainkan 5 game yang digunakan sebagai bahan uji coba.

LEARNING TO COOPERATE IN SOLVING THE TRAVELING SALESMAN PROBLEM

2005 ◽  
Vol 15 (01n02) ◽  
pp. 151-162 ◽  
Author(s):  
DEHU QI ◽  
RON SUN
Keyword(s):  
Reinforcement Learning ◽  
Traveling Salesman Problem ◽  
Single Agent ◽  
Traveling Salesman ◽  
Cooperative Team ◽  
The Traveling Salesman Problem ◽  
Better Than

A cooperative team of agents may perform many tasks better than single agents. The question is how cooperation among self-interested agents should be achieved. It is important that, while we encourage cooperation among agents in a team, we maintain autonomy of individual agents as much as possible, so as to maintain flexibility and generality. This paper presents an approach based on bidding utilizing reinforcement values acquired through reinforcement learning. We tested and analyzed this approach and demonstrated that a team indeed performed better than the best single agent as well as the average of single agents.

Sign in / Sign up

Export Citation Format

Share Document