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Especially, we argue that observational learning can emerge from pure Reinforcement Learning (RL), potentially coupled with memory. ... The later is naturally modeled by RL, by correlating the learning agent's reward with the teacher agent's behaviour. ... It demonstrates that observational learning can emerge from reinforcement learning, combined with memory. ...arXiv:1706.06617v1 fatcat:373blxc2rnfqvksiskyhcqezoy
The proposed agent was compared to the Q-learning and Adaptive Dynamic Programming based agents and demonstrated better ability to achieve goals in static observable deterministic gridworld environments ... Reinforcement-based agents have difficulties in transferring their acquired knowledge into new different environments due to the common identities-based percept representation and the lack of appropriate ... It combines Q-learning and supervised learning by learning Q-function with relational regression tree algorithm  . ...doi:10.5755/j01.itc.41.4.915 fatcat:5pgeedmyjbhdzgmidzvkyneh4i
Reinforcement learning (RL) models offer a framework to define changes in outcome expectations in a formal way by computing the prediction error (PE). ... RESULTS: Coupling to prediction errors in SAD was elevated in dorsomedial prefrontal cortex (DMPFC) when learning under observation. ... CC-BY-NC-ND 4.0 International license certified by peer review) is the author/funder. ...doi:10.1101/821512 fatcat:uraajs4nxjb65m3apwlr63uara
To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN). ... In addition, a stochastic policy can be learned through a maximum-entropy method with an adjustable temperature parameter. ...  applied Graph Convolutional Reinforcement Learning (DGN  ) in MBS. ...doi:10.3390/e24050638 fatcat:ol64sxrv65cshaw3ucrtc23l64
If partial observability can be overcome, these conditions suggest the use of reinforcement learning (RL). ... Instead, RL learns to map any observation to appropriate action (determined by a reward function), even if these observations do not lie on the original geometric state manifold. ... Reinforcement learning (RL) is a great candidate for this. ...doi:10.4173/mic.2022.1.1 fatcat:djyqwv5ylfe3valbupnkhrxz3a
I conducted two studies on the comparative effects of the observation of learn units during (a) reinforcement or (b) correction conditions on the acquisition of math objectives. ... The independent variables were the observation of reinforcement for correct responses as the control condition and the observation of corrections for incorrect responses. ... In addition to the expansion of communities of reinforcers by conditioning vocal praise for younger students via observation, research on learning academic skills via observation has been conducted with ...doi:10.7916/d8wq0b1x fatcat:o6qp3v756zgfbfa3eb3cm7rl2e
A two-step Viterbi decoding based on reinforcement learning is described. The idea is to strength or weaken HMM's by using Bayes-based confidence measure (BBCM) and distances between models. ... Correction component based on reinforcement learning The motivation is to apply the reinforcement learning principle to correct or re-estimate observation probabilities  . ... As was mentioned above, reinforcement learning could be applied as a correction in the observation probabilities. ...doi:10.21437/interspeech.2007-486 fatcat:vkexbogfcnbdvmzfqa7uve7uni
Through a modeling of four software robot agents' cooperative work to balance a ball on a plate, two social RL approaches-observing reinforcement and vicarious reinforcementare applied to individual RL ... This paper presents a study on Multiagent Reinforcement Learning (RL) for cooperating work from a social view to solve the problems of individual agent's incomplete world model, conflict of individual ... Acknowledgments This paper is supported in part by Center of Excellence for Research and Education on Complex Functional Mechanical Systems (COE program of the Ministry of Education, Culture, Sports, Science ...doi:10.9746/sicetr1965.40.328 fatcat:uf4mxseeejf7jnkzmsebxvbbta
The Behavior Analyst
cognitive mediation, and (3) vicarious reinforcement. ... Finally, the origin of observational learning is discussed in terms of recent data of neonatal imitation. ... The definition may stand only until we observe some longer-term reinforcement control of observational learning. ...doi:10.1007/bf03391892 pmid:22478602 pmcid:PMC2741741 fatcat:xioac3lf4fgobnpp36rg3awtrq
In data collection, researchers conducted interviews and observations on teachers of SMA Islam Negeri 2 Kuantan Singingi to determine the components of reinforcement used by teachers in learning English ... The reinforcement most often used by teachers in teaching English was reinforcement with words, while the reinforcement that was rarely used by teachers was token reinforcement. ... They stated that they often received token reinforcement in the learning process. To strengthen the results of the research, the researcher also collected the data by observation. ...doi:10.24036/ls.v1i1.4 fatcat:my6qgqga4va57cqlsn3ztbn4qi
Viewpoints in Teaching and Learning
Observational learning occurs when we learn by watching another person without being directly reinforced for learning and probably without any overt practice. ... First, let us be clear what we mean by observational learning. ...
This paper proposes a combination of Hierarchical Extreme learning machine and Reinforcement learning to find an optimal policy directly from visual input. ... This combination outperforms other methods in terms of accuracy and learning speed. The simulations and results were analysed by using MATLAB. ... Another system that uses raw visual input data is pole balancing controller, it was proposed to learn a control policy by using reinforcement learning  . ...doi:10.1088/1757-899x/184/1/012055 fatcat:eqchyfaskrbsbo6dmtz253fl3u
In this paper, we consider the question: can we perform reinforcement learning directly on experience collected by humans? ... RLV learns a policy and value function using experience collected by humans in combination with data collected by robots. ... This work was supported by ARL RCTA W911NF-10-2-0016, ARL DCIST CRA W911NF-17-2-0181, ONR grant N00014-20-1-2675, and by Honda Research Institute. ...arXiv:2011.06507v2 fatcat:twqtxfonrjcpteab4i4z6wmwqq
(1959) have demonstrated it in discrimination learning by mon- kevs. ... For S observes M trying to solve a problem by certain techniques and is more likely to use the same techniques when faced by a similar problem than if M had failed to solve the problem. ...
, and it dominates competing models such as the reinforcement of best response strategies. 2003 Published by Elsevier Science (USA). ... A general reinforcement model for continuous strategies, encompassing choice reinforcement learning, direction learning and payoff dependent imitation, performs well in explaining the experimental data ... Consider a general reinforcement learning model consisting of L different observational and experiential reinforcement rules. ...doi:10.1016/s0899-8256(03)00124-6 fatcat:ltmlqifnlje4vfdq73jwa3oza4
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