Linear Feature Encoding for Reinforcement Learning

  title={Linear Feature Encoding for Reinforcement Learning},
  author={Zhao Song and Ronald E. Parr and Xuejun Liao and Lawrence Carin},
Feature construction is of vital importance in reinforcement learning, as the quality of a value function or policy is largely determined by the corresponding features. The recent successes of deep reinforcement learning (RL) only increase the importance of understanding feature construction. Typical deep RL approaches use a linear output layer, which means that deep RL can be interpreted as a feature construction/encoding network followed by linear value function approximation. This paper… CONTINUE READING

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  • A. Geramifard, T. J. Walsh, N. Roy, J. How
  • UAI, 2013.
  • 2013
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