Essays about: "Episodic Learning"
Showing result 1 - 5 of 18 essays containing the words Episodic Learning.
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1. Model-based Residual Policy Learning for Sample Efficient Mobile Network Optimization
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Reinforcement learning is a powerful tool which enables an agent to learn how to control complex systems. However, during the early phases of training, the performance is often poor. READ MORE
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2. Role of Context in Episodic Memory : A Bayesian-Hebbian Neural Network Model of Episodic Recall
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Episodic memory forms a fundamental aspect of human memory that accounts for the storage of events as well as the spatio-temporal relations between events during a lifetime. These spatio-temporal relations in which episodes are embedded can be understood as their contexts. Contexts play a crucial role in episodic memory retrieval. READ MORE
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3. Attractor Neural Network modelling of the Lifespan Retrieval Curve
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Human capability to recall episodic memories depends on how much time has passed since the memory was encoded. This dependency is described by a memory retrieval curve that reflects an interesting phenomenon referred to as a reminiscence bump - a tendency for older people to recall more memories formed during their young adulthood than in other periods of life. READ MORE
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4. A quantitative analysis of how the Variational Continual Learning method handles catastrophic forgetting
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Catastrophic forgetting is a problem that occurs when an artificial neural network in the continual learning setting replaces historic information as additional information is acquired. Several methods claiming to handle the aforementioned problem are trained and evaluated using data sets with a small number of tasks, which does not represent a real continual learning situation where the number of tasks could be large. READ MORE
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5. Reinforcement Learning– Intelligent Weighting of Monte Carlo and Temporal Differences
University essay from Lunds universitet/Institutionen för reglerteknikAbstract : In Reinforcement learning the updating of the value functions determines the information spreading across the state/state-action space which condenses the valuebased control policy. It is important to have an information propagation across the value domain in a manner that is effective. READ MORE
