RLX2: TRAINING A SPARSE DEEP REINFORCEMENT LEARNING MODEL FROM SCRATCH

Abstract

Training deep reinforcement learning (DRL) models usually requires high computation costs. Therefore, compressing DRL models possesses immense potential for training acceleration and model deployment. However, existing methods that generate small models mainly adopt the knowledge distillation-based approach by iteratively training a dense network. As a result, the training process still demands massive computing resources. Indeed, sparse training from scratch in DRL has not been well explored and is particularly challenging due to non-stationarity in bootstrap training. In this work, we propose a novel sparse DRL training framework, "the Rigged Reinforcement Learning Lottery" (RLx2), which builds upon gradient-based topology evolution and is capable of training a DRL model based entirely on sparse networks. Specifically, RLx2 introduces a novel delayed multistep TD target mechanism with a dynamic-capacity replay buffer to achieve robust value learning and efficient topology exploration in sparse models. It also reaches state-of-the-art sparse training performance in several tasks, showing 7.5×-20× model compression with less than 3% performance degradation and up to 20× and 50× FLOPs reduction for training and inference, respectively.

1. INTRODUCTION

Deep reinforcement learning (DRL) has found successful applications in many important areas, e.g., games (Silver et al., 2017) , robotics (Gu et al., 2017) and nuclear fusion (Degrave et al., 2022) . However, training a DRL model demands heavy computational resources. For instance, AlphaGo-Zero for Go games (Silver et al., 2017) , which defeats all Go-AIs and human experts, requires more than 40 days of training time on four tensor processing units (TPUs). The heavy resource requirement results in expensive consumption and hinders the application of DRL on resource-limited devices. 2019) succeeded in generating ultimately sparse DRL networks. Yet, their approaches still require iteratively training dense networks, e.g., pre-trained dense teachers may be needed. As a result, the training cost for DRL remains prohibitively high, and existing methods cannot be directly implemented on resource-limited devices, leading to low flexibility in adapting the compressed DRL models to new environments, i.e., on-device models have to be retrained at large servers and re-deployed. Training a sparse DRL model from scratch, if done perfectly, has the potential to significantly reduce computation expenditure and enable efficient deployment on resource-limited devices, and achieves excellent flexibility in model adaptation. However, training an ultra sparse network (e.g., 90% sparsity) from scratch in DRL is challenging due to the non-stationarity in bootstrap training. Specifically, in DRL, the learning target is not fixed but evolves in a bootstrap way (Tesauro



Sparse networks, initially proposed in deep supervised learning, have demonstrated great potential for model compression and training acceleration of deep reinforcement learning. Specifically, in deep supervised learning, the state-of-the-art sparse training frameworks, e.g., SET (Mocanu et al., 2018) and RigL (Evci et al., 2020), can train a 90%-sparse network (i.e., the resulting network size is 10% of the original network) from scratch without performance degradation. On the DRL side, existing works including Rusu et al. (2016); Schmitt et al. (2018); Zhang et al. (

