RLx2: Training a Sparse Deep Reinforcement Learning Model from Scratch

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. 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). 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. 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.