Biological brains are inherently limited in their capacity to process and store information, but are nevertheless capable of solving complex tasks with apparent ease. Intelligent behavior is related to these limitations, since resource constraints drive the need to generalize and assign importance differentially to features in the environment or memories of past experiences. Recently, there have been parallel efforts in reinforcement learning and neuroscience to understand strategies adopted by artificial and biological agents to circumvent limitations in information storage. However, the two threads have been largely separate. In this article, we propose a dynamical framework to maximize expected reward under constraints of limited resources, which we implement with a cost function that penalizes precise representations of action-values in memory, each of which may vary in its precision. We derive from first principles an algorithm, Dynamic Resource Allocator (DRA), which we apply to two standard tasks in reinforcement learning and a model-based planning task, and find that it allocates more resources to items in memory that have a higher impact on cumulative rewards. Moreover, DRA learns faster when starting with a higher resource budget than what it eventually allocates for performing well on tasks, which may explain why frontal cortical areas in biological brains appear more engaged in early stages of learning before settling to lower asymptotic levels of activity. Our work provides a normative solution to the problem of learning how to allocate costly resources to a collection of uncertain memories in a manner that is capable of adapting to changes in the environment.

Weaknesses: (This section is being combined with "comments for improvement" section below) A bit of a nitpick regarding the language use around "more" or "less" resources. The authors write about an agent using "more resources", which corresponds to "lower entropy" for the actions in a particular state. I think, though, that technically (and literally, for this agent) the amount of resources used for each memory is exactly the same; it's literally a number to represent the mean and standard deviation. From what I can tell, the authors are arguing that memories with lower standard deviations would *require* more resources to represent in certain implementations (such as in brains). So it's not actually the case that more resources are used for low-sigma memories in the agent, but that more resources might be used in other agents.

This paper nicely bridges between neuroscience and RL, and considers the important topic of limited memory resources in RL agents. The topic is well-suited for NeurIPS (R2) as it has broader applicability toward e.g. All reviewers agreed that it is well-motivated and written (R1, R2, R3, R4), although R3 did ask for a bit more explanation on some methodological details. It is also appropriately situated with respect to related work (R1, R2, R3) although R2 suggests a separate related works section, and R4 wanted to see more discussion of work outside of neuroscience, focused on optimizing RL with limited capacity. R1 pointed out that perhaps there's a bit of confusion between memory precision and use of memory resources, as the former is more accurate for agents, the latter perhaps for real brains - ie more precise representations require more resources to encode in the brain, but this seems to be a minor point.

Large Language Models (LLMs) face computational inefficiencies and redundant processing when handling long context inputs, prompting a focus on compression techniques. While existing semantic vector-based compression methods achieve promising performance, these methods fail to account for the intrinsic information density variations between context chunks, instead allocating soft tokens uniformly across context chunks. This uniform distribution inevitably diminishes allocation to information-critical regions. To address this, we propose Dynamic Allocation of Soft Tokens (DAST), a simple yet effective method that leverages the LLM's intrinsic understanding of contextual relevance to guide compression. DAST combines perplexity-based local information with attention-driven global information to dynamically allocate soft tokens to the informative-rich chunks, enabling effective, context-aware compression. Experimental results across multiple benchmarks demonstrate that DAST surpasses state-of-the-art methods.

Biological brains are inherently limited in their capacity to process and store information, but are nevertheless capable of solving complex tasks with apparent ease. Intelligent behavior is related to these limitations, since resource constraints drive the need to generalize and assign importance differentially to features in the environment or memories of past experiences. Recently, there have been parallel efforts in reinforcement learning and neuroscience to understand strategies adopted by artificial and biological agents to circumvent limitations in information storage. However, the two threads have been largely separate. In this article, we propose a dynamical framework to maximize expected reward under constraints of limited resources, which we implement with a cost function that penalizes precise representations of action-values in memory, each of which may vary in its precision. We derive from first principles an algorithm, Dynamic Resource Allocator (DRA), which we apply to two standard tasks in reinforcement learning and a model-based planning task, and find that it allocates more resources to items in memory that have a higher impact on cumulative rewards.

TensorFlow vs Theano – TensorFlow, along with PyTorch, is currently the best known and most widely used machine learning framework. However, the choice of tool should never depend on one's own preferences, but should be adapted to the data to be examined. Especially in the Big data area, this can prevent a decisive loss of performance. It is therefore also worthwhile to look off the beaten track and to look at other frameworks and libraries in addition to the top dogs. Theano is one such open source Python library.

Christopher Crosbie is a Healthcare and Life Science Solutions Architect with Amazon Web Services. This post is co-authored by Gopal Wunnava, a Senior Consultant with AWS Professional Services. SparkR is an R package that allows you to integrate complex statistical analysis with large datasets. In this blog post, we introduce you running R with the Apache SparkR project on Amazon EMR. The diagram of SparkR below is provided as a reference, but this video provides an overview of what is depicted.