How can we build agents that keep learning from experience, quickly and efficiently, after their initial training? Here we take inspiration from the main mechanism of learning in biological brains: synaptic plasticity, carefully tuned by evolution to produce efficient lifelong learning. We show that plasticity, just like connection weights, can be optimized by gradient descent in large (millions of parameters) recurrent networks with Hebbian plastic connections. First, recurrent plastic networks with more than two million parameters can be trained to memorize and reconstruct sets of novel, high-dimensional 1000+ pixels natural images not seen during training. Crucially, traditional non-plastic recurrent networks fail to solve this task. Furthermore, trained plastic networks can also solve generic meta-learning tasks such as the Omniglot task, with competitive results and little parameter overhead. Finally, in reinforcement learning settings, plastic networks outperform a non-plastic equivalent in a maze exploration task. We conclude that differentiable plasticity may provide a powerful novel approach to the learning-to-learn problem.
Yet another approach is the MAML method @cite_12 , which performs gradient descent via backpropagation during the episode itself. In this case, the meta-learning consists in training the base network so that it can be fine-tuned'' easily and reliably by few steps (or even just one step) of additional gradient descent while performing the actual task.
@cite_12
2015174295
A bi-directional pressure relief valve includes a cylindrical main body portion defining a flow path through the valve and terminating in an asymmetrical lip portion having a curved contour about an axis of curvature extending perpendicularly to the flow path and a linear contour which is substantially normal to the curved contour. The lip portion has a normally closed slit formed through it and expands to open the slit to permit flow in one direction in response to pressure differentials across the lip portion above a first pressure differential, regains its undeformed shape to close the slit and check flow at pressure differentials below the first differential and above a second pressure differential, and collapses in a predictable and repeatable manner to open the slit to permit flow in an opposite direction at pressure differentials below the second pressure differential. A preferred embodiment includes an annular flange formed about the lip portion which facilitates the mounting of the valve in a conduit. If desired, the pressures at which flow can occur through the valve may be increased by providing the valve with stiffening ribs projecting radially outwardly of the main body portion.