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Deep Reinforcement Learning for Dexterous Manipulation with Concept Networks
[article]
2017
arXiv
pre-print
Deep reinforcement learning yields great results for a large array of problems, but models are generally retrained anew for each new problem to be solved. Prior learning and knowledge are difficult to incorporate when training new models, requiring increasingly longer training as problems become more complex. This is especially problematic for problems with sparse rewards. We provide a solution to these problems by introducing Concept Network Reinforcement Learning (CNRL), a framework which
arXiv:1709.06977v1
fatcat:4pqzm3g6c5dbzgvolphhag4bty