episodic reinforcement learning with associative memory

This model was extremely influential in the Drosophila memory field, but did not incorporate several important mammalian concepts, including ideas of separate episodic and semantic types of memory and Another aspect of the episodic memory is that it will automatically lead to discounting future value based on the number of episodic transitions that are necessary to reach the valued memory state. Overview of the proposed model. doi: 10.1016/B978-0-12-416008-8.00003-6. Chapter 3 Learning and Memory Bahman Moghimi (DBA, MBA) Consumer Behavior Buying, Having, and Being Sixth Edition 3-3 4. This yields pleasurable associations to warmth and relaxation and these positive associations will influence the choice. The two alternatives do not have any immediate evaluation but they associate to situations that do have value. where si is the salience input from the perceptual system and gi is feedback from the decision process. It is controlled by bottom up salience as well as top-down feedback from the decision mechanisms and selects which object is attended. There is also a spatial attention system that is responsible for directing attention to the different choices, indexing the accumulators based on the current spatial attention, and for locking on to the chosen object. Prior research illustrates that memory can guide value-based decision-making. doi: 10.1016/S1364-6613(00)01804-0, Baird, L. (1995). We want to propose that decisions like these are made not by direct evaluation of the item in front of us, but by imagining a future where we have made a particular choice (Atance and O'Neill, 2001; Schacter et al., 2017). Figure 9A shows the decision between two stimuli where one has an immediate value and the other is only indirectly associated with a value through a number of episodic associations, ranging from none to … Behav. doi: 10.1016/0014-4886(80)90159-4, Watkins, C. J., and Dayan, P. (1992). The activity of the accumulators can be made to influence the selection in the attention component. Associations of the third type have a longer latency and they produce episodic memory transitions (Herrmann et al., 1993). Pat loves searching for mushrooms, in particular chanterelles. The model also includes top-down feedback from the decision process to the attention system. In this paper, we propose Episodic Memory Deep Q-Networks (EMDQN), a novel reinforcement learning algo-rithm which uses episodic memory to supervise an agent's training. Augmenting experts with episodic memory, dedicated to recording observations and internal states, is a possible way to make learning more tractable Our experiments suggest that episodic memory can improve accuracy, sample efficiency and learning stability in single- and multi-agent settings Brain Sci. Affect. where Ii is the value from the value component when i is the accumulator selected by the input from the spatial attention system, and 0 otherwise. We have previously developed a model of memory processing that includes semantic, episodic and working memory in a comprehensive architecture. presented a model of how associative memories could be encoded and stored in the insect brain. Another feature is that choices are made faster but less correctly when the level of noise increases. This suggests that the amount of feed-forward inhibition can be used to control a trade-off between accuracy and speed in decision making (Wickelgren, 1977). We suggest that these two challenges are related. 8, 279–292. For example, playing a single game of Go is an episodic task, which you win or lose. Siyuan Li, Fangda Gu, Guangxiang Zhu, Chongjie Zhang: Context-Aware Policy Reuse. It would also be possible to include a number of additional associative connections. This function is similar to the value function in reinforcement learning when a linear function approximation from a binary state representation is used (Xu et al., 2014). This would be a case of satisfying rather than optimizing in decision making (Simon, 1972). We store the best historical values for Equal Contribution 1. A seemingly distinct challenge is that, cognitively, theories of RL have largely involved procedural and semantic memory, the way in which knowledge about action values or world models extracted gradually from many experiences can drive choice. The model does not sample the value of the product directly. As a consequence, it is more likely to win the competition and will also do so more quickly. 2006). View all Figure 1: Privileging stereotype consistency in how people deal with information, communicate with each other, and evaluate others. Such a strategy can be seen both in humans and in animals. To some extent, these are captured in the stereotypical images of these groups. Psychology mind map (LEARNING AND MEMORY (associative learning (classical…: Psychology mind map This may be sufficient to explain routine decisions but in general, we often collect evidence for different alternatives over time and take action only when sufficient evidence has been accumulated (Ratcliff et al., 2016). PsyArXiv. How visual attention and choice are affected by consumer preferences and properties of the supermarket shelf. Since contrast enhancement is associated with the effect of noradrenaline (NA) (Waterhouse and Woodward, 1980; Usher et al., 1999), this is in agreement with research indicating that NA is involved in decision making. In the simulations, we used a binary model of object salience, but the model is compatible with a more developed saliency map approach where target object are initially selected based on the visual salience. The model describes how semantic and episodic memory can be combined with a decision mechanism to choose between alternatives. 39:25. doi: 10.1037/h0072640, Joel, D., Niv, Y., and Ruppin, E. (2002). Close to the church there is a hotel. Going back to our pasta example, this could be choosing between two different pasta shapes from the same manufacturer or brand. This negative effect of successfulepisodicencodingwas alsoassociatedwithanattenuatedstriatalpredictionerrorsignal andincreasedconnectivitybetweenthehippocampusandthestriatum.Onepossibleinterpretation ofthisresultintermsofepisodicRListhat,becausethetrial-uniqueobjectswereentirelyincidental to the task, episodic evaluation … Isele and Cosgun [2018], for instance, explore different ways to populate a relatively large episodic memory for a continual RL setting where the learner does multiple passes over the data. Even the location of the item on the shelf, how hard it is to reach, or whether the shelf is full or not, may influence the decision. In the model, semantic associations depend on two mechanisms. Sutton, R. S., and Barto, A. G. (2018). Positive Feedback in Natural Systems, Vol. Evidence accumulation models: current limitations and future directions. Looking is buying. The model suggests that the discounting of future value is not governed by a decaying process during learning but is the result of episodic memories that are slower to influence the accumulators the more memory transitions are made before reaching a valued state. Experience Replay (ER) The use of ER is well established in reinforcement learning (RL) tasks [Mnih et al., 2013, 2015; Foerster et al., 2017; Rolnick et al., 2018]. It can also be used to activate associations that in turn may have positive or negative valuations. The focus here is on the interaction of these components rather than on learning of values or on initial storage in memory. doi: 10.1016/B978-1-55860-377-6.50013-X, Balkenius, C., Johansson, B., and Tjøstheim, T. A. This forces the memory state out of the current attractor and into a predicted future state. Res. The present model is indeed compatible with more elaborate models of classical conditioning. The excitatory value input is weighed by α before it reaches the accumulator. The first can be called “emotional” or “value” associations. doi: 10.3758/CABN.1.2.137, Ghallab, M., Nau, D., and Traverso, P. (2004). To improve sample efficiency of reinforcement learning, we propose a novel framework, called Episodic Reinforcement Learning with Associative Memory (ERLAM), which associates related experience trajectories to enable reasoning … Psychiatry 46, 1309–1320. Browse our catalogue of tasks and access state-of-the-art solutions. Richardson, D. C., and Spivey, M. J. Guangxiang Zhu focused on combining episodic control with reinforcement learning and his paper Episodic Reinforcement Learning with Associative Memory was also accepted. This component takes the current feature vector from the perceptual system as input and produces sequences of memory states based on previously learned associations. (2014). “Computational models of classical conditioning: a comparative study,” in From Animals to Animats 5, eds J.-A. To improve sample efficiency of reinforcement learning, we propose a novel framework, called Episodic Reinforcement Learning with Associative Memory (ERLAM), which associates related experience trajectories to enable reasoning effective strategies. (2019). Episodic memory is negatively correlated with reward learning, both across and within participants. PY101 Exam 3 Study Guide Learning Reflexes, Instincts, & Learning Non-Associative Learning Habituation Sensitization Read more... 2019. Reinforcement learning (RL) algorithms have made huge progress in recent years by leveraging the power of deep neural networks (DNN). The probability of switching attention to stimulus Oi at location i at each time step is. Sci. The effect of feed-forward inhibition is illustrated in Figure 5D. Figure 10. There is a negligible effect on the choice probabilities. We build a graph on top of states in memory based on state transitions and develop a reverse-trajectory propagation strategy to allow … 05/07/2019 ∙ by Artyom Y. Sorokin, et al. We do, however, not explore this feature of the memory system further. Synaptic depression is assumed to increase as a function of the signal flowing through the corresponding connection (Lerner et al., 2010; Aguilar et al., 2017; Balkenius et al., 2018). An unexpected consequence of episodic associations is that its interaction with the accumulator will cause future values to be discounted. The packaging now differs, as does the price. Acta Psychol. All parameters for the simulations are given in the Supplementary Material. Learning and memory of this association can be measured at various time points after training by testing flies by placing them at the choice point between odors A and B, and allowing them to choose between these odors. We show that when equipped with an episodic memory system inspired by theories of reinstatement and gating, the meta-learner learns to use the episodic and model-based learning … It is also used as a spatial index in the memory system and to select the appropriate accumulator for each choice. 67, 165–174. Figure 2: Motivational mechanisms that contribute to the reproduction of gender stereotypes. A slower accumulation decreases the probability the decision process will reach the decision threshold as a result of noise. Episodic Memory and Learning. The constant n is here set to 2. 1, 137–160. Please see our Privacy Policy. This method is similar to backward search in state space planning (Ghallab et al., 2004). doi: 10.1016/S0010-0277(00)00084-6, Schacter, D. L., Benoit, R. G., and Szpunar, K. K. (2017). However, a higher level of feed-forward inhibition will also lead to a longer reaction time. See text for further explanation. She may even explicitly remember a previous episode from a specific hotel and its proximity to a church. Rev. “Automatic and controlled processes in semantic priming: an attractor neural network model with latching dynamics,” in Proceedings of the Cognitive Science Society, Vol. An overreaching assumption of system level modeling is that it presents the overall organization of the different components and their dynamic interactions that determine many of the properties of the system. Rescorla, R. A., and Wagner, A. R. (1972). The basis for this mechanism is a delay imposed on the recurrent connections of the episodic memory (Sompolinsky and Kanter, 1986). 66, 470–493. Natl. (A) The two stimuli had value V(A) = 0.4 and V(B) = 0.6. Actor-critic models of the basal ganglia: new anatomical and computational perspectives. The latter reminds you of white seashells on an summer beach. J. Compar. In 2003, Martin Heisenberg et al. It is an auto-associative memory network means the trained network can recover full memorized information given only partial information as input. An episodic task lasts a finite amount of time. The second component is a decision mechanism that selects a particular action depending on the estimated value of the different actions. Sam knows nothing about mushrooms, but she has heard that there are chanterelles in a nearby forest so she offers to take Pat there. The model we present here is based on this assumption in the sense that each component is as simple as possible to exhibit the desired properties. Organ. Ikaros: building cognitive models for robots. Neurosci. There is also recurrent excitation weighted by γ and recurrent inhibition weighted by δ. doi: 10.1016/j.biopsych.2004.12.005, Chance, F. S., Abbott, L., and Reyes, A. D. (2002). doi: 10.1016/S0896-6273(02)00820-6. For example, previous work has implicated both working memory and procedural memory (i.e., reinforcement learning) in guiding choice. In this framework, vicarious trial and error is explained as an internal simulation that accumulates evidence for a particular choice. 27, 77–87. Adaptive Switching Circuits. Behav. Behav. The reaction time increases when the values of the two objects V(A) and V(B) are more similar as the activation of the accumulators takes longer when the values are lower (right). Psychiatry 57, 1416–1423. Cambridge, MA: MIT Press. A spatial attention component directs attention to the different choices and is also used to index different value accumulators that add up evidence for each alternative. Another aspect of the episodic memory is that it will automatically lead to discounting future value based on the number of episodic transitions that are necessary to reach the valued memory state. In particular, the longer the reaction time, the wider the distribution for the less preferred alternative becomes. Such a forward looking use of the episodic memory is similar to the forward sweeps found in animal brains as they consider different alternatives (Redish, 2016). Associations between value and spatial attention could bias the search process to particular locations and interactions between memory and spatial attention may enhance memory storage and recall (Balkenius et al., 2018). To the left, there are some alder trees so Pat immediately knows that the ground there is too wet for chanterelles. The first is a component that estimates the value of an action in a particular state. Instead of using a learned gradient of discounted value as in reinforcement learning, or a gradient set up by a specific goal as cognitive map models, the process starts with scanning the different alternatives and works toward a state with value (such as a goal state) through associating the properties of the observed alternatives with value of other memory states that have value. Rev. Sam knows from experiences of other small towns that it is probable that there is a hotel close to the church, a form of episodic memory. J. Adv. These are finally accumulated in the fourth component until a decision criterion is met and the system produces a choice as output. R. Soc. Choice probabilities and reaction times for different sequences of episodic transitions. Sci. Soc. 493, 99–110. In classical learning theory, stimulus-response chains are learned at the goal and gradually extended to a sequence leading from start to goal. The model has a number of attractive properties: When perceptual states are directly associated with value through the memory component, the model reduces to the value function of a reinforcement learning system (Sutton and Barto, 2018), or critic of an actor-critic architecture (Joel et al., 2002). We tested a situation in which alternative A and B both have two attributes. U.S.A. 104, 1726–1731. Neural Inf. A single alternative is processed at a time in the flow from perception to valuation, while the spatial attention component keeps track of the different alternatives and makes sure that their values are separately processed by the accumulators. Tolman, E. C., and Honzik, C. H. (1930). As some of the sources of information used in the construction of episodic memories are external to the original event, memory accuracy suffers. (2003). Suddenly she remembers the old rule of thumb “Cherchez léglise”—search for the church—and as she sees the church tower now and then from the streets, she manages to find the church. PDF ; Guangxiang Zhu *, Jianhao Wang*, Zhizhou Ren*, and Chongjie Zhang. Inform. The gray arrows represent interactions that we do not address in this paper. Q-learning. Neuron 35, 773–782. Simon, H. A. When the gain of this feedback is increased, choices are faster and the system will look more at the alternative that will finally be chosen (Figure 10). However, since value is used here in a sequential accumulation process (described below), it is not necessary that the value component supports higher order conditioning, which is otherwise the basis for chaining in reinforcement learning. There is thus no specific discounting mechanism in the model. This would include, for example, remembering the name of someone or the aroma of a particular perfume. f is the activation function of the nodes (ReLU). The episodic associations have a longer time constant τ that makes the network jump between states. However, little progress has been made in understanding when specific memory systems help more than others and how well they generalize. In the simulation, the input to each accumulator decreased as the values become more similar, because they are assumed to sum to 1 which makes the decision slower. 261, 1–31. Behav. We review the psychology and neuroscience of reinforcement learning (RL), which has experienced significant progress in the past two decades, enabled by the comprehensive experimental study of simple learning and decision-making tasks. doi: 10.1016/j.cobeha.2017.06.002, Schmajuk, N. A., and Thieme, A. These values are used by a selection mechanism to decide which action to take. One shape is the pipe-like penne, while the other is the sea-shell-like conchiglie. The Google Brain team with DeepMind and ETH Zurich have introduced an episodic memory-based curiosity model which allows Reinforcement Learning (RL) agents to explore environments in an intelligent way. We show how the new model explains both simple immediate choices, choices that depend on multiple sensory factors and complicated selections between alternatives that require forward looking simulations based on episodic and semantic memory structures. Reinforcement learning and episodic memory in humans and animals: an integrative framework. Neurol. Her mobile phone is out of battery and the car does not have a GPS. … (B,C) The spatial index from the attention system is used as a selection mechanism that directs the value to one accumulator per object. Latching dynamics in neural networks with synaptic depression. There is even a list of ingredients in small print that may give additional information. Mean response times, variability, and skew in the responding of ADHD children: a response time distributional approach. 6, 114–133. Received: 07 May 2020; Accepted: 16 November 2020; Published: 10 December 2020. The past 25 years of research has established the complexity of the ...Read More. Secondly, in computational neuroscience, the idea of neural competition is a cornerstone of many theories of brain function (Amari, 1977; Grossberg et al., 1978; Erlhagen and Schöner, 2002). ∙ 0 ∙ share Episodic memory plays an important role in the behavior of animals and humans. Episodic Reinforcement Learning with Associative Memory. Both goals have value 1. Eng. Participants completed a task … Biol. But our brains seem able to form and use autobiographical memories that allow for easy recollection of the past, and can infer complex dependencies between our past actions and their eventual outcomes. This means that we may also decide that it does not matter which particular choice is made. Figure 9. The accumulator consists of integrators indexed by spatial attention. Lett. doi: 10.1007/BF00337259, Aston-Jones, G., and Cohen, J. D. (2005). Each of the states in the sequence can have its own semantic or value associations. Unlike in traditional reinforcement learning, the goal gradient is here set up dynamically by activating the goal state and the activity is then propagated to other states depending on how closely associated they are with the goal. The accumulator and decision mechanisms thus implement a selection policy over the different perceived objects in the environment. J. Econ. Figure 1: The original Baddeley & Hitch (1974) working memory model. (A) Increased noise (sigma) gives more random choices (left) and faster reaction time (right) for the two objects A and B where the value of A is 0.4 and the value of B is 0.6. When both values are available immediately, the model will mostly select stimulus B, but as the number of memory transitions needed increases, the model will become more likely to select the immediate lower reward. Erlhagen, W., and Schöner, G. (2002). Varieties of attention-deficit/hyperactivity disorder-related intra-individual variability. As the memory system transitions through a sequence of states, the value system calculates the value of each state and sends the result to the accumulator described below. It was implemented using the Ikaros framework for system-level brain modeling (Balkenius et al., 2010, 2020). We tested the model's ability to sum contributions from individual attributes, and as expected the model selected each of the alternatives with probability 0.5 (Figure 7). For simplicity, we have not included these inputs in the equations here. Sci. This leads to the prediction that an alternative that contains less details and thus produces less transitions should be favored over an alternative that produces many transitions given that the values are the same. In this experiment participants viewed pairs of words on a monitor and heard the same words used in a sentence; they were required to judge the likelihood of what was reported in the sentence. 1 3-1 2. In this case, the complete system will allocate more time to the alternative that looks best so far in the evaluation. In particular, projected scenarios lacked spatial coherence. I present an account of the origins and development of the multicomponent approach to working memory, making a distinction between the overall theoretical framework, which has remained relatively stable, and the attempts to build more specific models ...Read More. For example, let’s say we have a network consists of 10 neurons connected to each other. The first is direct low latency associations represented by the wji with a low value of τji in Equation (1). Episodic Reinforcement Learning with Associative Memory (ERLAM), which associates related ex-perience trajectories to enable reasoning effective strategies. Like other models of choice, the model can handle a situation where there are two objects with one attribute each. Figure 1. We focus on: Semantic-based profile for researchers; Integrating academic data; Accurately searching the heterogeneous network; Analyz Such competitive processes in the brain are modulated by arousal that can make the competition more or less random (Aston-Jones et al., 1999; Usher et al., 1999; Mather et al., 2016) and shift between exploration and exploitation (Gilzenrat et al., 2010). The processes of the memory system are also influenced by modulating signals from arousal systems that can determine the level of randomness of the state transitions (Aston-Jones et al., 1999; Chance et al., 2002; Aston-Jones and Cohen, 2005). This type of memory deals specifically with the relationship between these different objects or concepts. Appetite 116, 29–38. λ is a decay constant and N(σ) is a normally distributed noise term. Psychol., 10 December 2020 In simple cases, each visible attribute of the package may add to the evaluation in a direct way. Another useful property of the memory model is that it can not only recall earlier episodes, but also produce new combinations of previous memories using random transitions between similar memory states (Balkenius et al., 2018). Figure 3: Comparison of the mixed conditions of the Dots (now called Hearts and Flowers) and Simon tasks in percentage of correct responses (based on Davidson et al. Such correlation is part of the semantic memory. 10. doi: 10.1162/089976698300017502, Usher, M., Cohen, J. D., Servan-Schreiber, D., Rajkowski, J., and Aston-Jones, G. (1999). B Biol. CB, TT, BJ, AW, and PG planned the paper, the theoretical framework, and wrote the paper. It has been observed that many organisms use an excessive amount of time to make decisions between similar alternatives, where too much time is allocated to a choice relative to what is gained by making the correct choice (Oud et al., 2016). This entails setting up simulations where the model is allowed to interact with an environment where it can learn semantic, episodic and value associations. However, here we use a unified memory state rather than distinguishing between the “what” and “where” systems of the earlier model. Their main feature is that any sensory input will give rise to a sequence of internal memory states that starts from the features of the attended object. Feedback excitation has the effect of decreasing response time because it will produce a positive feedback to the accumulators (Figure 5E).

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