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Skip to Search Results- 2Continual Learning
- 1Generate and Test
- 1Lifelong Learning
- 1Machine Learning
- 1Neural Network Pruning
- 1Reinforcement Learning
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Spring 2023
Gradient Descent algorithms suffer many problems when learning representations using fixed neural network architectures, such as reduced plasticity on non-stationary continual tasks and difficulty training sparse architectures from scratch. A common workaround is continuously adapting the neural...
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Fall 2024
If we aspire to design algorithms that can run for long periods, continually adapting to new, unexpected situations, then we must be willing to deploy our agents without tuning their hyperparameters over the agent’s entire lifetime. The standard practice in deep RL—and even continual RL—is to...