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Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired controlmethods to tackle the intricacies associated with controlling complex robotic systems. [ABSTRACT FROM AUTHOR]

Digital engineering and digital twins are increasingly being used in nuclear energy projects with important impacts. At Idaho National Laboratory, these approaches have been applied in a variety of nuclear energy research, development, and demonstration projects, with key lessons and evolutions occurring for each. In this paper, we describe the use of digital engineering and digital twins in the Versatile Test Reactor design, National Reactor Innovation Center test beds, and nonproliferation analysis of the AGN-201 reactor design. We share key lessons learned for these projects related to tool selection, adoption and training, and working with existing assets versus beginning at the design phase. We also share highlights of future potential uses of digital twins and digital engineering, including using artificial intelligence to perform repetitive design tasks and digital twins to move towards semiautonomous nuclear power plant operations. [ABSTRACT FROM AUTHOR]

Complex robotic systems, such as humanoid robot hands, soft robots, and walking robots, pose a challenging control problem due to their high dimensionality and heavy non-linearities. Conventional model-based feedback controllers demonstrate robustness and stability but struggle to cope with the escalating system design and tuning complexity accompanying larger dimensions. In contrast, data-driven methods such as artificial neural networks excel at representing high-dimensional data but lack robustness, generalization, and real-time adaptiveness. In response to these challenges, researchers are directing their focus to biological paradigms, drawing inspiration from the remarkable control capabilities inherent in the human body. This has motivated the exploration of new control methods aimed at closely emulating the motor functions of the brain given the current insights in neuroscience. Recent investigation into these Brain-Inspired control techniques have yielded promising results, notably in tasks involving trajectory tracking and robot locomotion. This paper presents a comprehensive review of the foremost trends in biomimetic brain-inspired control methods to tackle the intricacies associated with controlling complex robotic systems.

Digital engineering and digital twins are increasingly being used in nuclear energy projects with important impacts. At Idaho National Laboratory, these approaches have been applied in a variety of nuclear energy research, development, and demonstration projects, with key lessons and evolutions occurring for each. In this paper, we describe the use of digital engineering and digital twins in the Versatile Test Reactor design, National Reactor Innovation Center test beds, and nonproliferation analysis of the AGN-201 reactor design. We share key lessons learned for these projects related to tool selection, adoption and training, and working with existing assets versus beginning at the design phase. We also share highlights of future potential uses of digital twins and digital engineering, including using artificial intelligence to perform repetitive design tasks and digital twins to move towards semiautonomous nuclear power plant operations.

Experience replay is widely used in AI to bootstrap reinforcement learning (RL) by enabling an agent to remember and reuse past experiences. Classical techniques include shuffled-, reversed-ordered- and prioritized-memory buffers, which have different properties and advantages depending on the nature of the data and problem. Interestingly, recent computational neuroscience work has shown that these techniques are relevant to model hippocampal reactivations recorded during rodent navigation. Nevertheless, the brain mechanisms for orchestrating hippocampal replay are still unclear. In this paper, we present recent neurorobotics research aiming to endow a navigating robot with a neuro-inspired RL architecture (including different learning strategies, such as model-based (MB) and model-free (MF), and different replay techniques). We illustrate through a series of numerical simulations how the specificities of robotic experimentation (e.g., autonomous state decomposition by the robot, noisy perception, state transition uncertainty, non-stationarity) can shed new lights on which replay techniques turn out to be more efficient in different situations. Finally, we close the loop by raising new hypotheses for neuroscience from such robotic models of hippocampal replay. [ABSTRACT FROM AUTHOR]

Experience replay is widely used in AI to bootstrap reinforcement learning (RL) by enabling an agent to remember and reuse past experiences. Classical techniques include shuffled-, reversed-ordered- and prioritized-memory buffers, which have different properties and advantages depending on the nature of the data and problem. Interestingly, recent computational neuroscience work has shown that these techniques are relevant to model hippocampal reactivations recorded during rodent navigation. Nevertheless, the brain mechanisms for orchestrating hippocampal replay are still unclear. In this paper, we present recent neurorobotics research aiming to endow a navigating robot with a neuro-inspired RL architecture (including different learning strategies, such as model-based (MB) and model-free (MF), and different replay techniques). We illustrate through a series of numerical simulations how the specificities of robotic experimentation (e.g., autonomous state decomposition by the robot, noisy perception, state transition uncertainty, non-stationarity) can shed new lights on which replay techniques turn out to be more efficient in different situations. Finally, we close the loop by raising new hypotheses for neuroscience from such robotic models of hippocampal replay.

Patterson et al. explored how early life stress relates to "stimulus-response habits", defined as "instrumental behaviors that... have come to be automatically elicited by stimuli in whose presence the behavior has been repeatedly performed, without regard to instrumental outcomes." Habit, automaticity, motivation, reinforcement learning, model-free, model-based Keywords: habit; automaticity; motivation; reinforcement learning; model-free; model-based EN habit automaticity motivation reinforcement learning model-free model-based 1 6 6 10/25/21 20211021 NES 211021 There is wanting to do something, and there is actually doing it. In addition to the daily behaviors studied by McCloskey and Johnson ([27]), simple behaviors, such as a phone-app-based motor sequence, also become more automatic with daily practice (Banca et al.). [Extracted from the article]

It is now commonly accepted that instrumental actions can reflect goal-directed control; i.e., they can show sensitivity to changes in the relationship to and the value of their consequences. With overtraining, stress, neurodegeneration, psychiatric conditions, or after exposure to various drugs of abuse, goal-directed control declines and instrumental actions are performed independently of their consequences. Although this latter insensitivity has been argued to reflect the development of habitual control, the lack of a positive definition of habits has rendered this conclusion controversial. Here we consider various alternative definitions of habit, including recent suggestions they reflect chunked action sequences, to derive criteria with which to categorize responses as habitual. We consider various theories regarding the interaction between goal-directed and habitual controllers and propose a collaborative model based on their hierarchical integration. We argue that this model is consistent with the available data, can be instantiated both at an associative level and computationally and generates interesting predictions regarding the influence of this collaborative integration on behavior.

It is now commonly accepted that instrumental actions can reflect goal-directed control; i.e., they can show sensitivity to changes in the relationship to and the value of their consequences. With overtraining, stress, neurodegeneration, psychiatric conditions, or after exposure to various drugs of abuse, goal-directed control declines and instrumental actions are performed independently of their consequences. Although this latter insensitivity has been argued to reflect the development of habitual control, the lack of a positive definition of habits has rendered this conclusion controversial. Here we consider various alternative definitions of habit, including recent suggestions they reflect chunked action sequences, to derive criteria with which to categorize responses as habitual. We consider various theories regarding the interaction between goal-directed and habitual controllers and propose a collaborative model based on their hierarchical integration. We argue that this model is consistent with the available data, can be instantiated both at an associative level and computationally and generates interesting predictions regarding the influence of this collaborative integration on behavior. [ABSTRACT FROM AUTHOR]

Reward-based decision making is thought to be driven by at least two different types of decision systems: a simple stimulus–response cache-based system which embodies the common-sense notion of “habit,” for which model-free reinforcement learning serves as a computational substrate, and a more deliberate, prospective, model-based planning system. Previous work has shown that loss aversion, a well-studied measure of how much more on average individuals weigh losses relative to gains during decision making, is reduced when participants take all possible decisions and outcomes into account including future ones, relative to when they myopically focus on the current decision. Model-based control offers a putative mechanism for implementing such foresight. Using a well-powered data set (N = 117) in which participants completed two different tasks designed to measure each of the two quantities of interest, and four models of choice data for these tasks, we found consistent evidence of a relationship between loss aversion and model-based control but in the direction opposite to that expected based on previous work: loss aversion had a positive relationship with model-based control. We did not find evidence for a relationship between either decision system and risk aversion, a related aspect of subjective utility.

Do habits play a role in our social impressions? To investigate the contribution of habits to the formation of social attitudes, we examined the roles of model-free and model-based reinforcement learning in social interactions – computations linked in past work to habit and planning, respectively. Participants in this study learned about novel individuals in a sequential reinforcement learning paradigm, choosing financial advisors who led them to high- or low-paying stocks. Results indicated that participants relied on both model-based and model-free learning, such that each type of learning was expressed in both advisor choices and post-task self-reported liking of advisors. Specifically, participants preferred advisors who could provide large future rewards as well as advisors who had provided them with large rewards in the past. Although participants relied more heavily on model-based learning overall, they varied in their use of model-based and model-free learning strategies, and this individual difference influenced the way in which learning related to self-reported attitudes: among participants who relied more on model-free learning, model-free social learning related more to post-task attitudes. We discuss implications for attitudes, trait impressions, and social behavior, as well as the role of habits in a memory systems model of social cognition. [ABSTRACT FROM AUTHOR]

Current theories suggest that decision-making arises from multiple, competing action-selection systems. Rodent studies dissociate deliberation and procedural behavior, and find a transition from procedural to deliberative behavior with experience. However, it remains unknown how this transition from deliberative to procedural control evolves within single trials, or within blocks of repeated choices. We adapted for rats a two-step task which has been used to dissociate model-based from model-free decisions in humans. We found that amixture ofmodel-based andmodel-free algorithms was more likely to explain rat choice strategies on the task than either model-based or model-free algorithms alone. This task contained two choices per trial, which provides a more complex and non-discrete per-trial choice structure. This task structure enabled us to evaluate how deliberative and procedural behavior evolved within-trial and within blocks of repeated choice sequences. We found that vicarious trial and error (VTE), a behavioral correlate of deliberation in rodents, was correlated between the two choice points on a given lap. We also found that behavioral stereotypy, a correlate of procedural automation, increased with the number of repeated choices. While VTE at the first choice point decreased with the number of repeated choices, VTE at the second choice point did not, and only increased after unexpected transitions within the task. This suggests that deliberation at the beginning of trialsmay correspond to changes in choice patterns, while mid-trial deliberation may correspond to an interruption of a procedural process.

Current theories suggest that decision-making arises from multiple, competing action-selection systems. Rodent studies dissociate deliberation and procedural behavior, and find a transition from procedural to deliberative behavior with experience. However, it remains unknown how this transition from deliberative to procedural control evolves within single trials, or within blocks of repeated choices. We adapted for rats a two-step task which has been used to dissociate model-based from model-free decisions in humans.We found that amixture ofmodel-based andmodel-free algorithms was more likely to explain rat choice strategies on the task than either model-based or model-free algorithms alone. This task contained two choices per trial, which provides a more complex and non-discrete per-trial choice structure. This task structure enabled us to evaluate how deliberative and procedural behavior evolved within-trial and within blocks of repeated choice sequences. We found that vicarious trial and error (VTE), a behavioral correlate of deliberation in rodents, was correlated between the two choice points on a given lap. We also found that behavioral stereotypy, a correlate of procedural automation, increased with the number of repeated choices. While VTE at the first choice point increased with the number of repeated choices, VTE at the second choice point did not, and only increased after unexpected transitions within the task. This suggests that deliberation at the beginning of trials may correspond to changes in choice patterns, while mid-trial deliberation may correspond to an interruption of a procedural process. [ABSTRACT FROM AUTHOR]

Instrumental learning and decision-making rely on two parallel systems: a goal-directed and a habitual system. In the past decade, several paradigms have been developed to study these systems in animals and humans by means of e.g. overtraining, devaluation procedures and sequential decision-making. These different paradigms are thought to measure the same constructs, but cross-validation has rarely been investigated. In this study we compared two widely used paradigms that assess aspects of goal-directed and habitual behavior. We correlated parameters from a two-step sequential decision-making task that assesses model-based and model-free learning with a slips-of-action paradigm that assesses the ability to suppress cue-triggered, learnt responses when the outcome has been devalued and is therefore no longer desirable. Model-based control during the two-step task showed a very moderately positive correlation with goal-directed devaluation sensitivity, whereas model-free control did not. Interestingly, parameter estimates of model-based and goal-directed behavior in the two tasks were positively correlated with higher-order cognitive measures (e.g. visual short-term memory). These cognitive measures seemed to (at least partly) mediate the association between model-based control during sequential decision-making and goal-directed behavior after instructed devaluation. This study provides moderate support for a common framework to describe the propensity towards goal-directed behavior as measured with two frequently used tasks. However, we have to caution that the amount of shared variance between the goal-directed and model-based system in both tasks was rather low, suggesting that each task does also pick up distinct aspects of goal-directed behavior. Further investigation of the commonalities and differences between the model-free and habit systems as measured with these, and other, tasks is needed. Also, a follow-up cross-validation on the neural systems driving these constructs across different paradigms would promote the definition and operationalization of measures of instrumental learning and decision-making in humans.

Instrumental learning and decision-making rely on two parallel systems: a goal-directed and a habitual system. In the past decade, several paradigms have been developed to study these systems in animals and humans by means of e.g., overtraining, devaluation procedures and sequential decision-making. These different paradigms are thought to measure the same constructs, but cross-validation has rarely been investigated. In this study we compared two widely used paradigms that assess aspects of goal-directed and habitual behavior. We correlated parameters from a two-step sequential decision-making task that assesses model-based (MB) and model-free (MF) learning with a slips-of-action paradigm that assesses the ability to suppress cue-triggered, learnt responses when the outcome has been devalued and is therefore no longer desirable. MB control during the two-step task showed a very moderately positive correlation with goal-directed devaluation sensitivity, whereas MF control did not show any associations. Interestingly, parameter estimates of MB and goal-directed behavior in the two tasks were positively correlated with higher-order cognitive measures (e.g., visual short-term memory). These cognitive measures seemed to (at least partly) mediate the association between MB control during sequential decision-making and goal-directed behavior after instructed devaluation. This study provides moderate support for a common framework to describe the propensity towards goal-directed behavior as measured with two frequently used tasks. However, we have to caution that the amount of shared variance between the goal-directed and MB system in both tasks was rather low, suggesting that each task does also pick up distinct aspects of goal-directed behavior. Further investigation of the commonalities and differences between the MF and habit systems as measured with these, and other, tasks is needed. Also, a follow-up cross-validation on the neural systems driving these constructs across different paradigms would promote the definition and operationalization of measures of instrumental learning and decision-making in humans. [ABSTRACT FROM AUTHOR]

Although prosocial behaviors have been widely studied across disciplines, the mechanisms underlying them are not fully understood. Evidence from psychology, biology and economics suggests that prosocial behaviors can be driven by a variety of seemingly opposing factors: altruism or egoism, intuition or deliberation, inborn instincts or learned dispositions, and utility derived from actions or their outcomes. Here we propose a framework inspired by research on reinforcement learning and decision making that links these processes and explains characteristics of prosocial behaviors in different contexts. More specifically, we suggest that prosocial behaviors inherit features of up to three decision-making systems employed to choose between self- and other- regarding acts: a goal-directed system that selects actions based on their predicted consequences, a habitual system that selects actions based on their reinforcement history, and a Pavlovian system that emits reflexive responses based on evolutionarily prescribed priors. This framework, initially described in the field of cognitive neuroscience and machine learning, provides insight into the potential neural circuits and computations shaping prosocial behaviors. Furthermore, it identifies specific conditions in which each of these three systems should dominate and promote other- or self- regarding behavior.

The tendency to make unhealthy choices is hypothesized to be related to an individual's temporal discount rate, the theoretical rate at which they devalue delayed rewards. Furthermore, a particular form of temporal discounting, hyperbolic discounting, has been proposed to explain why unhealthy behavior can occur despite healthy intentions. We examine these two hypotheses in turn. We first systematically review studies which investigate whether discount rates can predict unhealthy behavior. These studies reveal that high discount rates for money (and in some instances food or drug rewards) are associated with several unhealthy behaviors and markers of health status, establishing discounting as a promising predictive measure. We secondly examine whether intention-incongruent unhealthy actions are consistent with hyperbolic discounting. We conclude that intention-incongruent actions are often triggered by environmental cues or changes in motivational state, whose effects are not parameterized by hyperbolic discounting. We propose a framework for understanding these state-based effects in terms of the interplay of two distinct reinforcement learning mechanisms: a model-based (or goal-directed) system and a model-free (or habitual) system. Under this framework, while discounting of delayed health may contribute to the initiation of unhealthy behavior, with repetition, many unhealthy behaviors become habitual; if health goals then change, habitual behavior can still arise in response to environmental cues. We propose that the burgeoning development of computational models of these processes will permit further identification of health decision-making phenotypes.

Although prosocial behaviors have been widely studied across disciplines, the mechanisms underlying them are not fully understood. Evidence from psychology, biology and economics suggests that prosocial behaviors can be driven by a variety of seemingly opposing factors: altruism or egoism, intuition or deliberation, inborn instincts or learned dispositions, and utility derived from actions or their outcomes. Here we propose a framework inspired by research on reinforcement learning and decision making that links these processes and explains characteristics of prosocial behaviors in different contexts. More specifically, we suggest that prosocial behaviors inherit features of up to three decision-making systems employed to choose between self- and otherregarding acts: a goal-directed system that selects actions based on their predicted consequences, a habitual system that selects actions based on their reinforcement history, and a Pavlovian system that emits reflexive responses based on evolutionarily prescribed priors. This framework, initially described in the field of cognitive neuroscience and machine learning, provides insight into the potential neural circuits and computations shaping prosocial behaviors. Furthermore, it identifies specific conditions in which each of these three systems should dominate and promote other- or self- regarding behavior. [ABSTRACT FROM AUTHOR]

Traditional theories of decision-making assume that utilities are based on the intrinsic value of outcomes; in turn, these values depend on associations between expected outcomes and the current motivational state of the decision-maker. This view disregards the fact that humans (and possibly other animals) have prospection abilities, which permit anticipating future mental processes and motivational and emotional states. For instance, we can evaluate future outcomes in light of the motivational state we expect to have when the outcome is collected, not (only) when we make a decision. Consequently, we can plan for the future and choose to store food to be consumed when we expect to be hungrier, not immediately. Furthermore, similarly to any expected outcome, we can assign a a value to our anticipated mental processes and emotions. It has been reported that (in some circumstances) human subjects prefer to receive an unavoidable punishment immediately, probably because they are anticipating the dread associated with the time spent waiting for the punishment. This article offers a formal framework to guide neuroeconomic research on how prospection affects decision-making. The model has two characteristics. First, it uses model-based Bayesian inference to describe anticipation of cognitive and motivational processes. Second, the utility maximization process considers these anticipations in two ways: to evaluate outcomes (e.g., the pleasure of eating a pie is evaluated differently at the beginning of a dinner, when one is hungry, and at the end of the dinner, when one is satiated), and as outcomes having a value themselves (e.g., the case of dread as a cost of waiting for punishment). By explicitly accounting for the relationship between prospection and value, our model provides a framework to reconcile the utility-maximization approach with psychological phenomena such as planning for the future and dread.

Decisions can arise in different ways, such as a gut feeling, doing what worked last time, or planful deliberation. Different decision-making systems are dissociable behaviorally, map onto distinct brain systems, and require different computational demands. For instance, ’model-free’ decision strategies use prediction errors to estimate scalar action values from previous experience, while ’model-based’ strategies leverage internal forward models to generate and evaluate potentially rich outcome expectancies. Animal learning studies indicate that expectancies may arise from different sources, including not only forward models but also Pavlovian associations, and the flexibility with which such representations impact behavior may depend on how they are generated. In the light of these considerations, we review the results of van der Meer and Redish (2009a), who found that ventral striatal neurons that respond to reward delivery can also be activated at other points, notably at a decision point where hippocampal forward representations were also observed. These data suggest the possibility that ventral striatal reward representations contribute to model-based expectancies used in deliberative decision-making.