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What is state of the art in reinforcement learning right now? https://arxiv.org/abs/1602.01783 https://arxiv.org/abs/1602.01783 Is there a way to deal with "s

• visarga 10y ago
  That paper was 10 months ago. There have been many RL papers in the meantime, but sparsity is only a problem with respect to reward, not state or action, from what I can see.
  • Hydraulix989 10y ago
    You didn't answer my question. :(
• sapphireblue 10y ago
  Looks like the "UNREAL" (https://arxiv.org/abs/1611.05397 https://arxiv.org/abs/1611.05397), "Learning to reinforcement learn" (https://arxiv.org/abs/1611.05763 https://arxiv.org/abs/1611.05763) and "RL^2" (https://arxiv.org/abs/1611.02779 https://arxiv.org/abs/1611.02779) are state of art in pure RL for now. Finally there is a trend of using recurrent neural network as a top component of the Q-network. Perhaps we will see even more sophisticated RNNs like DNC and Recurrent Entity Networks applied here. Also we'll see meta-reinforcement learning applied to a curriculum of environments.
  • Hydraulix989 10y ago
    The crazy thing is that these stacked model architectures are starting to become another layer of "lego blocks" so to speak.