  Deep reinforcement learning (DRL) has achieved remarkable success across
various domains, such as video games, robotics, and, recently, large language
models. However, the computational costs and memory requirements of DRL models
often limit their deployment in resource-constrained environments. The
challenge underscores the urgent need to explore neural network compression
methods to make RDL models more practical and broadly applicable. Our study
investigates the impact of two prominent compression methods, quantization and
pruning on DRL models. We examine how these techniques influence four
performance factors: average return, memory, inference time, and battery
utilization across various DRL algorithms and environments. Despite the
decrease in model size, we identify that these compression techniques generally
do not improve the energy efficiency of DRL models, but the model size
decreases. We provide insights into the trade-offs between model compression
and DRL performance, offering guidelines for deploying efficient DRL models in
resource-constrained settings.
