Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
Results
Showing 1 to 11 of 11 for “"Credit Assignment"”.
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Competitive and Noncompetitive Credit Assignment to Reward Cues in the Lateral Orbitofrontal Cortex
… the mechanisms underlying variations in credit assignment to reward cues, with a focus on the lateral orbitofrontal cortex (lOFC). The lOFC has long been implicated in signaling reward expectancies, with controversy as to whether it signals the economic value of future outcomes, their …
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Learning to teach and meta-learning for sample-efficient multiagent reinforcement learning
… by other challenges, including the multiagent credit assignment, the high dimensionality of the problems, and the lack of convergence guarantees. As a result, many experiences are often required to learn effective multiagent policies. This thesis introduces two frameworks to reduce the sample …
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Axiomatic Explanations for Visual Search, Retrieval, and Similarity Learning
… behavior. We show that the theory of fair credit assignment provides a unique axiomatic solution that generalizes several existing recommendation- and metric-explainability techniques in the literature. Using this formalism, we show when existing approaches violate “fairness” and derive …
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Towards a unified multi-agent reinforcement learning framework
… parameter sharing, communication mechanisms, and credit assignment, thereby enhancing overall system efficiency and adaptability. Finally, we develop a comprehensive MARL library that standardizes environment and algorithm integration, facilitating the flexible mapping of policies and streamlined …
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Gaining Perspective with an Evolutionary Cognitive Architecture for Intelligent Agents
… noise reduction, generalization, memory, and credit-assignment. A wide set of tests that target these components are used to test the general capabilities of Brain Evolver as well as some more high-level tests that abstractly simulate space rover mission tasks. The notion of perspective and …
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Investigations into controllers for adaptive autonomous agents based on artificial neural networks.
… composite of the well-known action selection and credit assignment problems. The solution was demonstrated in two sets of simulation studies involving variants of each architecture. These studies also showed: firstly that the expected advantages over the standard behaviour-based approach were …
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Learning temporal and structural credit assignments for reinforcement learning and experimental design
Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-05-01
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An optimization approach to relate neural circuit architecture, loss landscapes and learning performance in static and dynamic tasks
… behavior of a circuit, which is known as the credit assignment problem. Rather than looking at single neurons or synapses then, one must step back and look at the overall circuit architecture and match the circuit’s structural patterns to its behavior. One pattern of notable importance that we …
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Aligning Models for Human-Centric Decision Systems
… and presenting a new method for improved credit assignment that stabilises and accelerates training. In each case we conduct an investigation of the topic, provide algorithmic solutions for the challenge at hand, and validate proposals through experiments on both simulations and real-world …
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Graph Neural Networks for Multi-Agent Learning
… In our final work, we use GNNs to tackle the credit assignment problem in multi-agent reinforcement learning. Leveraging the decentralised manner in which GNNs combine local and aggregated neighbouring information, we perform value factorisation with a GNN-based architecture. This approach …
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CLOSED-LOOP SCALING: AUTONOMOUS IMPROVEMENT OF LLM AND LVLM REASONING
As human-curated data approaches exhaustion, sustaining the improvement of large language models (LLMs) and large vision--language models (LVLMs) demands a paradigm shift. This thesis proposes automatic scaling: a closed-loop framework in which models autonomously improve through their own …