• Corpus ID: 238354404

Temporal Shift Reinforcement Learning

@inproceedings{Thomas2021TemporalSR,
  title={Temporal Shift Reinforcement Learning},
  author={Deepak George Thomas and Tichakorn Wongpiromsarn and Ali Jannesari},
  year={2021}
}
The function approximators employed by traditional image-based Deep Reinforcement Learning (DRL) algorithms usually lack a temporal learning component and instead focus on learning the spatial component. We propose a technique, Temporal Shift Reinforcement Learning (TSRL), wherein both temporal, as well as spatial components are jointly learned. Moreover, TSRL does not require additional parameters to perform temporal learning. We show that TSRL outperforms the commonly used frame stacking… 

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