Forager is a lightweight partially-observable continual RL environment that exposes loss of plasticity in current agents and highlights the value of state construction for ongoing learning.
arXiv preprint arXiv:2403.00514 , year=
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Smooth-Leaky and Randomized Smooth-Leaky activations mitigate loss of plasticity in continual learning by targeting negative-branch shape and saturation behavior.
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Forager: a lightweight testbed for continual learning with partial observability in RL
Forager is a lightweight partially-observable continual RL environment that exposes loss of plasticity in current agents and highlights the value of state construction for ongoing learning.
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Activation Function Design Sustains Plasticity in Continual Learning
Smooth-Leaky and Randomized Smooth-Leaky activations mitigate loss of plasticity in continual learning by targeting negative-branch shape and saturation behavior.