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    • Enhancing Exploration in Reinforcement Learning through Multi-Step Actions 

      Medini, Tharun (2020-12-03)
      The paradigm of Reinforcement Learning (RL) has been plagued by slow and uncertain training owing to the poor exploration in existing techniques. This can be mainly attributed to the lack of training data beforehand. Further, querying a neural network after every step is a wasteful process as some states are conducive to multi-step actions. Since we ...