Leveraging Knowledge Distillation for Efficient Deep Reinforcement
  Learning in Resource-Constrained Environments
Guanlin Meng
This paper aims to explore the potential of combining Deep Reinforcement
Learning (DRL) with Knowledge Distillation (KD) by distilling various DRL
algorithms and studying their distillation effects. By doing so, the
computational burden of deep models could be reduced while maintaining the
performance. The primary objective is to provide a benchmark for evaluating the
performance of different DRL algorithms that have been refined using KD
techniques. By distilling these algorithms, the goal is to develop efficient
and fast DRL models. This research is expected to provide valuable insights
that can facilitate further advancements in this promising direction. By
exploring the combination of DRL and KD, this work aims to promote the
development of models that require fewer GPU resources, learn more quickly, and
make faster decisions in complex environments. The results of this research
have the capacity to significantly advance the field of DRL and pave the way
for the future deployment of resource-efficient, decision-making intelligent
systems.