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Continual Learning as Computationally Constrained Reinforcement Learning
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An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills over a long lifetime could advance the frontier of artificial intelligence capabilities. The design of such agents, which remains a long-standing challenge of artificial intelligence, is addressed by the subject of continual learning. This monograph clarifies and formalizes concepts of continual learning, introducing a framework and set of tools to stimulate further research.
Forward citations
Cited by 4 Pith papers
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Capacity-Constrained Continual Learning
An LQG predictor constrained to keep at most B bits of information about its observation history is optimally solved by a rate-distortion compressed Kalman estimate, with water-filling capacity allocation across subsystems.
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Mitigating Plasticity Loss in Continual Reinforcement Learning by Reducing Churn
Reducing churn in continual RL via C-CHAIN prevents NTK rank collapse and substantially improves learning across four benchmark suites.
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Optimizers Qualitatively Alter Solutions And We Should Leverage This
Deep learning optimizers should be designed to induce desired solution properties, not just convergence speed; different optimizers demonstrably land in qualitatively different minima.
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Memory Allocation in Resource-Constrained Reinforcement Learning
Memory allocation between model and plan affects performance in memory-constrained RL, with a balanced split often optimal.
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