First Experiments with PowerPlay

@article{Srivastava2013FirstEW,
  title={First Experiments with PowerPlay},
  author={R. Srivastava and Bas R. Steunebrink and J. Schmidhuber},
  journal={Neural networks : the official journal of the International Neural Network Society},
  year={2013},
  volume={41},
  pages={
          130-6
        }
}
Like a scientist or a playing child, POWERPLAY (Schmidhuber, 2011) not only learns new skills to solve given problems, but also invents new interesting problems by itself. By design, it continually comes up with the fastest to find, initially novel, but eventually solvable tasks. It also continually simplifies or compresses or speeds up solutions to previous tasks. Here we describe first experiments with POWERPLAY. A self-delimiting recurrent neural network SLIM RNN (Schmidhuber, 2012) is used… Expand
PowerPlay: Training an Increasingly General Problem Solver by Continually Searching for the Simplest Still Unsolvable Problem
One Big Net For Everything
C OMPETITIVE EXPERIENCE REPLAY
Meta-learning curiosity algorithms
Hindsight Experience Replay
Optimal Curiosity-Driven Modular Incremental Slow Feature Analysis
Competitive Experience Replay
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