ライフサイエンスコーパス: reinforcement learning

1 te population models) and learning dynamics (reinforcement learning).

2 e control based on the outcome of retrieval (reinforcement learning).

3 "model-free" and "model-based" strategies in reinforcement learning.

4 ant consequences for computational models of reinforcement learning.

5 ntribution of these regions to probabilistic reinforcement learning.

6 neural control of instrumental behaviors by reinforcement learning.

7 d opposing effects on locomotor behavior and reinforcement learning.

8 hoice outcomes are incrementally updated via reinforcement learning.

9 acquire long-term reward predictions through reinforcement learning.

10 spatial attention that occurs during spatial reinforcement learning.

11 with an important role for these neurons in reinforcement learning.

12 -dimensional sensory inputs using end-to-end reinforcement learning.

13 considered to carry reward-related signal in reinforcement learning.

14 ia to solve the "curse of dimensionality" in reinforcement learning.

15 arallel contribution of MB and MF systems in reinforcement learning.

16 e D1A did not affect perceptual inference or reinforcement learning.

17 fic algorithms, such as prediction errors in reinforcement learning.

18 h a behavioral measure of opponent-selective reinforcement learning.

19 encode the full range of RPEs necessary for reinforcement learning.

20 engthening action-reward associations during reinforcement learning.

21 ode in circuits underpinning both affect and reinforcement learning.

22 physiological and neurochemical correlate of reinforcement learning.

23 ors to appetitively and aversively motivated reinforcement learning.

24 ncies between expected and actual rewards in reinforcement learning.

25 credit assignment problems in the context of reinforcement learning.

26 were used as electrophysiological indices of reinforcement learning.

27 as correlating with quantities derived from reinforcement learning.

28 cated in motivational outcome evaluation and reinforcement learning.

29 rcuits are important for action selection or reinforcement learning.

30 is important for optimal decision making and reinforcement learning.

31 roduce effects of dopaminergic medication on reinforcement learning.

32 CC behavioral patterns could be explained by reinforcement learning.

33 mine (DA) and regulates appetitive drive and reinforcement learning.

34 r to deviate sharply from the predictions of reinforcement learning.

35 udy failed to replicate previous findings on reinforcement learning.

36 ntial hypothesis posits that dopamine biases reinforcement learning.

37 sure may be a critical cellular component of reinforcement learning.

38 licit influence of movement error signals on reinforcement learning.

39 responses consistent with such "hierarchical reinforcement learning."

40 e neural dynamics of choice processes during reinforcement learning?

41 When trained with reinforcement learning, a DNC can complete a moving bloc

42 emory interference toward the enhancement of reinforcement learning across multiple timescales.

43 In reinforcement learning, action values are increased when

44 cent evidence indicates that, beyond classic reinforcement learning adaptations, individuals may also

45 While reinforcement learning agents have achieved some success

46 s' behaviour was better explained by a basic reinforcement learning algorithm, adults' behaviour inte

47 a (BG) have been hypothesized to implement a reinforcement learning algorithm.

48 However, reinforcement learning algorithms are notorious for not

49 bine them with model-free, experience-based, reinforcement learning algorithms to train the gliders.

50 dopaminergic neurons and temporal difference reinforcement learning algorithms.

51 well captured by model-free and model-based reinforcement learning algorithms.

52 l prediction errors as defined in model-free reinforcement learning algorithms.

53 In inverse reinforcement learning an observer infers the reward dis

54 MRI, we found that adolescents showed better reinforcement learning and a stronger link between reinf

55 Theories of reinforcement learning and approach behavior suggest tha

56 nformation-processing system responsible for reinforcement learning and appropriate decision making.

57 triatal DA D2 receptors (D2Rs) also regulate reinforcement learning and are implicated in glucose-rel

58 ons relative to outcomes are both central to reinforcement learning and are thought to underlie finan

59 Here, we demonstrate that both reinforcement learning and context memory facilitate att

60 This is true for reinforcement learning and decision making (where the la

61 the representation of "state," in studies of reinforcement learning and decision making, and also in

62 have important implications for theories of reinforcement learning and delay discounting.

63 rcement learning and a stronger link between reinforcement learning and episodic memory for rewarding

64 pamine is thought to play a critical role in reinforcement learning and goal-directed behavior, but i

65 problem through a harmonious combination of reinforcement learning and hierarchical sensory processi

66 ghly implicated both as a teaching signal in reinforcement learning and in motivating actions to obta

67 ptive function in this context, and also how reinforcement learning and incentive salience models may

68 ventral tegmental area (VTA) helps regulate reinforcement learning and motivated behavior, in part b

69 ion of corticostriatal circuitry involved in reinforcement learning and motivation, although the inte

70 nd decision-making is often cast in terms of reinforcement learning and optimal decision theory.

71 des a neural basis for persisting effects in reinforcement learning and placebo hypoalgesia.

72 Here we examine reinforcement learning and sensitivity to both reward an

73 sms that underlie these processes, including reinforcement learning and spike-timing-dependent plasti

74 d role for D3 receptors in select aspects of reinforcement learning and suggest that individual varia

75 the engagement of these two brain regions in reinforcement learning and their respective roles are fa

76 trated that the core brain areas involved in reinforcement learning and valuation, such as the ventra

77 We review the evidence from reinforcement-learning and habit-learning studies in TS,

78 s role in positively motivated behaviors and reinforcement learning, and its dysfunction in addiction

79 ge current reward prediction error models of reinforcement learning, and suggest that classical anima

80 ed with behavioral and neural dysfunction in reinforcement learning, and whether such dysfunction is

81 uences for brain and computational models of reinforcement learning are discussed.SIGNIFICANCE STATEM

82 by which different computational elements of reinforcement learning are dynamically coordinated acros

83 test whether the neural correlates of human reinforcement learning are sensitive to experienced risk

84 theorists have recently proposed model-based reinforcement learning as a candidate framework.

85 is research has established the viability of reinforcement learning as a model of behavioral adaptati

86 We here propose a representation of reinforcement learning as a stochastic process in finite

87 ynaptic transmission that in turn drives the reinforcement learning associated with the first alcohol

88 The resulting architecture includes striatal reinforcement learning based on egocentric representatio

89 ipheral glucose levels and glucose-dependent reinforcement learning behaviors and highlight the notio

90 based fMRI to the learning rate parameter in reinforcement learning, both in theory and in two previo

91 subtraction, a computation that is ideal for reinforcement learning but rarely observed in the brain.

92 at sensory processing, sequence learning and reinforcement learning, but are limited in their ability

93 the relative influence of the two systems in reinforcement learning, but few studies have manipulated

94 Dopamine signaling is implicated in reinforcement learning, but the neural substrates target

95 strengthen action-reward associations during reinforcement learning, but their role in human learning

96 ence suggests that the basal ganglia support reinforcement learning by adjusting action values accord

97 st that cerebellar damage indirectly impairs reinforcement learning by increasing motor noise, but do

98 e if response conflict acts as a cost during reinforcement learning by modulating experienced reward

99 error signal proposed to support model-free reinforcement learning, cached-value errors are typicall

100 n which the players learn based on a type of reinforcement learning called experience-weighted attrac

101 showing that individuals adopting a type of reinforcement learning, called aspiration learning, phen

102 arise from two computational strategies for reinforcement learning, called model-free and model-base

103 In particular, learning from reward, or reinforcement learning, can be driven by two distinct co

104 A model of reinforcement learning captured the dose-dependent effec

105 ned pain behavior depends on two dissociable reinforcement learning circuits.

106 In several reinforcement learning contexts, such as Pavlovian condi

107 principle forms the basis for computational reinforcement learning controllers, which have been frui

108 n several time-dependent functions including reinforcement learning, decision making, and interval ti

109 Reinforcement learning describes how the brain can choos

110 stigated how opponent identity affects human reinforcement learning during a simulated competitive ga

111 al dopamine signaling, which is critical for reinforcement learning, efficient mobilization of effort

112 We briefly review the neuroscience of reinforcement learning, emphasizing the representational

113 These results demonstrate that reinforcement learning engages both attentional habits a

114 erent types of game and the possibility that reinforcement learning explains observed behavior.

115 Traditional models of such reinforcement learning focus on learning about the mean

116 odel that augments the standard reward-based reinforcement learning formulation by associating a valu

117 s formal description of avoidance within the reinforcement learning framework provides a new means of

118 We further describe a reinforcement learning framework through which the tutor

119 rvised learning from human expert games, and reinforcement learning from games of self-play.

120 escribe inferences based on a combination of reinforcement learning from past feedback and participan

121 sed learning from human expert moves, and by reinforcement learning from self-play.

122 an instrumental loss-avoidance and win-gain reinforcement learning functional magnetic resonance ima

123 Computational modeling of trial-by-trial reinforcement learning further indicated that lower OFC

124 Although reinforcement learning has been central in explaining pl

125 The role of dopamine in reinforcement learning has been extensively studied, but

126 Such reinforcement learning has been shown to engage a specif

127 Reinforcement Learning has greatly influenced models of

128 years, ideas from the computational field of reinforcement learning have revolutionized the study of

129 yield choice patterns similar to model-free reinforcement learning; however, samples can vary from t

130 behavior may relate to those in hierarchical reinforcement learning (HRL), a machine-learning framewo

131 ze two apparently distinct functions, one in reinforcement learning (i.e., prediction error) and anot

132 ansformation, namely, its ability to enhance reinforcement learning in a dynamic environment.

133 the DA D2 receptor in a behavioral study of reinforcement learning in a sample of 78 healthy male vo

134 eal an important role for the hippocampus in reinforcement learning in adolescence and suggest that r

135 echanism explaining risk-taking and impaired reinforcement learning in BD.

136 he operation of the neural system underlying reinforcement learning in humans.

137 ssing striatal projection neurons influenced reinforcement learning in mice.

138 implementations) suggests that the study of reinforcement learning in organisms will be best served

139 l link between IL-6 and striatal RPEs during reinforcement learning in the context of acute psycholog

140 mportant prerequisite for action control and reinforcement learning in the human brain.

141 of these tasks ignores important aspects of reinforcement learning in the real world: (a) State spac

142 iting advances in the theory and practice of reinforcement learning, including developments in fundam

143 Reinforcement learning is a branch of machine learning c

144 Reinforcement learning is an adaptive process in which a

145 hypothesis and how they interact to produce reinforcement learning is unknown.

146 FICANCE STATEMENT In aversive and appetitive reinforcement learning, learned effects show extinction

147 phrenia (SZ) show deficits on tasks of rapid reinforcement learning, like probabilistic reversal lear

148 h tasks is very diverse, but that a class of reinforcement learning-like models that use a mixture of

149 c DA D2 receptors in motivational aspects of reinforcement learning may apply to humans as well.

150 al studies, the present results suggest that reinforcement learning may be a major proximate mechanis

151 tterns correlate with behaviorally estimated reinforcement learning measures.

152 ing is not accounted for by varying a single reinforcement learning mechanism, but by changing the se

153 end-guided choice can be modelled by using a reinforcement-learning mechanism that computes a longer-

154 egulation as resulting from a self-adjusting reinforcement-learning mechanism that infers latent stat

155 engagement are captured by a self-adjusting reinforcement-learning mechanism that tracks changing en

156 These associations can be attained with reinforcement learning mechanisms using a reward predict

157 iatal plasticity can be induced by classical reinforcement learning mechanisms, and might be central

158 de song and speech development by disrupting reinforcement learning mechanisms.

159 in driving behavior on the basis of classic reinforcement learning mechanisms.

160 ), a machine-learning framework that extends reinforcement-learning mechanisms into hierarchical doma

161 more simplistic learning as reflected by the reinforcement learning model (exceedance probability, Px

162 hierarchical Bayesian model with an advanced reinforcement learning model and by comparing the model

163 i delivered at random times and formulated a reinforcement learning model based on belief states.

164 all learning curves could be obtained with a reinforcement learning model endowed with a multiplicati

165 s behavioral and neural data compared with a reinforcement learning model inspired by rating systems

166 mood and anxiety group on a parameter of our reinforcement learning model that characterizes a prepot

167 n a multiple choice foraging task and used a reinforcement learning model to quantify explore-exploit

168 al responses, we designed a similarity-based reinforcement learning model wherein prediction errors g

169 To cope with uncertainty, we extended a reinforcement learning model with a belief state about t

170 ious BG components may be described within a reinforcement learning model, in which a broad repertoir

171 are better explained in a context-dependent reinforcement learning model.

172 onally distinct forms of predictive model: a reinforcement-learning model of the environment obtained

173 We formulated our proposal in a reinforcement-learning model, and we showed that our mod

174 their inferences over time, we pitted simple reinforcement learning models against more specific "com

175 ic reinforcement learning task combined with reinforcement learning models and fMRI, we found that ad

176 Most reinforcement learning models assume that the reward sig

177 tal midline theta, and have implications for reinforcement learning models of cognitive control.

178 stimuli, not actions.SIGNIFICANCE STATEMENT Reinforcement learning models of the ventral striatum (V

179 Reinforcement learning models provide formal and testabl

180 Despite these robust behavioral effects, reinforcement learning models reliant on reward predicti

181 ly, these responses were sufficient to train reinforcement learning models to predict the behaviorall

182 We found that, across different conditions, reinforcement learning models were approximately as accu

183 Reinforcement learning models were used to explicate obs

184 iction errors underlie learning of values in reinforcement learning models, are represented by phasic

185 One class of reinforcement learning models, known as the actor-critic

186 nly to the effect of learning rate in simple reinforcement learning models, we provide a template for

187 sk evaluating performance using Bayesian and Reinforcement learning models.

188 Using insights from computational reinforcement-learning models and basic-science studies

189 only used and neurophysiologically motivated reinforcement-learning models to simulated behavioral da

190 ity can further improve learning by having a reinforcement learning module separate from sensory proc

191 skills more and because they have a separate reinforcement learning module.

192 ional mechanisms, model-based and model-free reinforcement learning, neuronally implemented in fronto

193 responding for sucrose pellets and sustained reinforcement learning of glucose-paired flavors.

194 ions may relate to abnormal decision making, reinforcement learning or somatic processing in TS.

195 Adolescents and adults carried out a novel reinforcement learning paradigm in which participants le

196 To address this, we used a reinforcement learning paradigm to independently manipul

197 ers using a well-characterized probabilistic reinforcement learning paradigm.

198 Unlike in traditional reinforcement learning paradigms, in our experiment the

199 icated that in such an unstable environment, reinforcement learning parameters are downregulated depe

200 rediction error and incentive value, two key reinforcement learning parameters, before and after meth

201 We approach this puzzle from a reinforcement learning perspective: what kind of spatial

202 mechanism between model-based and model-free reinforcement learning, placing such a mechanism within

203 Computational theories of reinforcement learning play a central role in the newly

204 Many accounts of decision making and reinforcement learning posit the existence of two distin

205 warranted to determine if disrupted positive reinforcement learning predicts high-risk behavior follo

206 ion could then be used in collaboration with reinforcement learning principles toward an autonomous b

207 on error (RPE) signal that is used in such a reinforcement learning process.

208 Dopamine plays a key role in reinforcement learning processes.

209 ts that two components are involved in these reinforcement learning processes: a critic, which estima

210 To identify the reinforcement-learning processes that are affected by ch

211 d drug use, may be because of disruptions in reinforcement-learning processes that enable behavior to

212 The theory of reinforcement learning provides a normative account, dee

213 ce striatal hyperdopaminergia and changes in reinforcement learning relevant to schizophrenia.

214 vioral task affects the neural mechanisms of reinforcement learning remains incompletely understood.

215 s compatible with model-free and model-based reinforcement learning, reports the subjective rather th

216 o learn and retain novel skills, but optimal reinforcement learning requires a balance between explor

217 Modification of spatial attention via reinforcement learning requires the integration of rewar

218 ans trained on a wide range of tasks support reinforcement learning (RL) algorithms as accounting for

219 hrough a process described using model-based reinforcement learning (RL) algorithms.

220 Models of reinforcement learning (RL) are prevalent in the decisio

221 parate literatures have examined dynamics of reinforcement learning (RL) as a function of experience

222 of the current task state, which is used for reinforcement learning (RL) elsewhere in the brain.

223 hoices can be characterized by extending the reinforcement learning (RL) framework to incorporate age

224 Reinforcement learning (RL) has become a dominant comput

225 Model-based and model-free reinforcement learning (RL) have been suggested as algor

226 Reinforcement learning (RL) in simple instrumental tasks

227 Reinforcement learning (RL) is the behavioral process of

228 ed fMRI analysis revealed a fractionation of reinforcement learning (RL) signals in the ventral stria

229 g a novel oculomotor paradigm, combined with reinforcement learning (RL) simulations, we show that mo

230 ng the basal ganglia- and dopamine-dependent reinforcement learning (RL) system, but also prefrontal

231 Reinforcement learning (RL) theories posit that dopamine

232 eract during learning.SIGNIFICANCE STATEMENT Reinforcement learning (RL) theory has been remarkably p

233 Reinforcement learning (RL) theory posits that learning

234 might be expected if they were using a naive reinforcement learning (RL) update of value.

235 ble systems, such as working memory (WM) and reinforcement learning (RL), contribute simultaneously t

236 We review the psychology and neuroscience of reinforcement learning (RL), which has experienced signi

237 ed blocks of stimulus-based and action-based reinforcement learning (RL).

238 ectories were well characterized by a simple reinforcement-learning (RL) model that maintained and co

239 or signals consistent with formal models of "reinforcement learning" (RL) have repeatedly been found

240 asic activity of dopamine neurons represents reinforcement learning's temporal difference prediction

241 A reinforcement-learning scheme we demonstrate is capable

242 reased ventral striatal RPE signaling during reinforcement learning (session 2), though there was no

243 We formalize this mechanism in a reinforcement learning setting, illustrate its costs and

244 uring the effective delivery of dopaminergic reinforcement learning signals broadcast to the striatum

245 w that dopamine-dependent mechanisms enhance reinforcement learning signals in the striatum and sharp

246 ve evaluation bias may be driven by aberrant reinforcement learning signals, which fail to update fut

247 ons can be regulated via striatally mediated reinforcement learning signals.

248 sing a combination of evolutionary analysis, reinforcement learning simulations, and behavioral exper

249 The striatum is known to play a key role in reinforcement learning, specifically in the encoding of

250 We then consider model-free reinforcement learning strategies that exploit correlati

251 ble of processing both positive and negative reinforcement learning strategies.

252 This is different from the Pavlov, a reinforcement learning strategy promoting mutual coopera

253 To use reinforcement learning successfully in situations approa

254 ipiprazole on more cognitive facets of human reinforcement learning, such as learning from the forgon

255 Using a probabilistic reinforcement learning task combined with reinforcement

256 The present fMRI study scanned a reinforcement learning task in which participants stop a

257 n counterfactual learning, we administered a reinforcement learning task that involves both direct le

258 retrospectively predicted performance on the reinforcement learning task, demonstrating that the bias

259 The player performed a probabilistic reinforcement learning task, receiving information about

260 During a reinforcement learning task, the information content of

261 event-related potentials in humans during a reinforcement learning task, we show strong evidence in

262 We studied patients in a two-stage reinforcement learning task, while they were ON and OFF

263 of value representation following a standard reinforcement learning task.

264 performance of two groups of participants on reinforcement learning tasks using a computational model

265 Contrary to previous results in reinforcement learning tasks, individuals with moderate

266 In typical reinforcement learning tasks, subjects should base subse

267 to encode a reward prediction error (RPE) in reinforcement learning tasks.

268 patients with schizophrenia are impaired in reinforcement learning tasks.

269 ion approach in humans with two well-studied reinforcement learning tasks: one isolating model-based

270 Q-learning is a method of reinforcement learning that employs backwards stagewise

271 and norepinephrine neurotransmission support reinforcement learning, the role of dopamine has been em

272 ular decision-making task used in studies of reinforcement learning, the two-armed bandit task.

273 between expected and actual outcomes), with reinforcement learning theories being based on predictio

274 Reinforcement learning theories formalize this map as a

275 cur without external feedback, yet normative reinforcement learning theories have difficulties explai

276 unifying framework encompassing Bayesian and reinforcement learning theories of associative learning.

277 Does learning in human observers comply with reinforcement learning theories, which describe how subj

278 One prominent theory, the reinforcement learning theory by Holroyd and colleagues,

279 According to reinforcement learning theory of decision making, reward

280 In particular, the reinforcement learning theory of the ERN may need to be

281 Reinforcement learning theory powerfully characterizes h

282 This view, rooted in reinforcement learning theory, equates motor variability

283 tance of action exploration, a key idea from reinforcement learning theory, showing that motor variab

284 Using computational modeling based on reinforcement learning theory, we found that conditionin

285 Wagner learning rule, a finding predicted by reinforcement learning theory.

286 error conforming to classical principles of reinforcement learning theory.

287 ncode reward prediction errors and can drive reinforcement learning through their projections to stri

288 ate their expectations after playing a DG by reinforcement learning to construct a model that explain

289 the DG and also to the wide applicability of reinforcement learning to explain many strategic interac

290 Reinforcement learning uncovers those sensorimotor cues

291 while also shedding light onto mechanisms of reinforcement learning using realistic biological assump

292 tal plasticity and individual differences in reinforcement learning, was found to predict the effect

293 l neuroimaging and computational modeling of reinforcement learning, we demonstrate positive habenula

294 Indicative of reinforcement learning, we found that estimated trial-by

295 to the responses of dopamine neurons during reinforcement learning, which have been shown to encode

296 to differentiate model-based from model-free reinforcement learning, while generating neurophysiologi

297 strate that bidding is well characterized by reinforcement learning with biased reward representation

298 To avoid confounding basic reinforcement learning with strategic conscious reasonin

299 stem is prominently implicated in model-free reinforcement learning, with fMRI BOLD signals in ventra

300 re we introduce an algorithm based solely on reinforcement learning, without human data, guidance or