3260 papers • 126 benchmarks • 313 datasets
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These leaderboards are used to track progress in developmental-learning-11
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Use these libraries to find developmental-learning-11 models and implementations
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It is illustrated the computational efficiency of IMGEPs as these robotic experiments use a simple memory-based low-level policy representations and search algorithm, enabling the whole system to learn online and incrementally on a Raspberry Pi 3.
A deep neural model is proposed which is designed in the light of different aspects of developmental learning of emotional concepts to provide an integrated solution for internal and external emotion appraisal and is able to generate internal emotional concepts that evolve through time.
The ability of the presented approach to bootstrap the learned behavior from a simpler system to a more complex robot (comparatively higher-dimensional action-space) and reach better performance faster is shown.
The purpose is to model the development of an artificial organism that exhibits complex behaviors that will generate behaviors ranging from stimulus responses to group behavior that resembles collective motion and considers future applications of the developmental neurosimulation approach.
The total number of supervised weight updates can be substantially reduced using three complementary strategies: first, it is found that only 2% of supervised updates are needed to achieve ~80% of the match to adult ventral stream, while using two orders of magnitude fewer supervised synaptic updates.
This approach provides a framework for adaptive agent behavior that might result from a developmental approach: namely by exploiting critical periods or growth and acquisition, an explicitly embodied network architecture, and a distinction between the assembly of neuronal networks and active learning on these networks.
Over a classic evolutionary search aimed at finding good gaits for tentacle 2D robots, this work adds a developmental process over the robots’ morphologies and shows that this produces better and qualitatively different gaits than an evolutionary search with only adult robots, and that it prevents premature convergence by fostering exploration.
Adding a benchmark result helps the community track progress.