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Loss of plasticity in deep continual learning
Artificial neural networks, deep-learning methods and the backpropagation algorithm form
the foundation of modern machine learning and artificial intelligence. These methods are …
the foundation of modern machine learning and artificial intelligence. These methods are …
Simplifying deep temporal difference learning
M Gallici, M Fellows, B Ellis, B Pou, I Masmitja… - ar** for deep continual and reinforcement learning
Many failures in deep continual and reinforcement learning are associated with increasing
magnitudes of the weights, making them hard to change and potentially causing overfitting …
magnitudes of the weights, making them hard to change and potentially causing overfitting …
Normalization and effective learning rates in reinforcement learning
Normalization layers have recently experienced a renaissance in the deep reinforcement
learning and continual learning literature, with several works highlighting diverse benefits …
learning and continual learning literature, with several works highlighting diverse benefits …
Learning continually by spectral regularization
Loss of plasticity is a phenomenon where neural networks can become more difficult to train
over the course of learning. Continual learning algorithms seek to mitigate this effect by …
over the course of learning. Continual learning algorithms seek to mitigate this effect by …
[PDF][PDF] In value-based deep reinforcement learning, a pruned network is a good network
Recent work has shown that deep reinforcement learning agents have difficulty in effectively
using their network parameters. We leverage prior insights into the advantages of sparse …
using their network parameters. We leverage prior insights into the advantages of sparse …
No representation, no trust: connecting representation, collapse, and trust issues in ppo
Reinforcement learning (RL) is inherently rife with non-stationarity since the states and
rewards the agent observes during training depend on its changing policy. Therefore …
rewards the agent observes during training depend on its changing policy. Therefore …