Expert Commentary: Accelerating Stochastic Policy Gradient in Reinforcement Learning with Negative Momentum
In the field of reinforcement learning (RL), stochastic optimization algorithms like stochastic policy gradient (SPG) have shown great promise. However, one major challenge remains: how to quickly acquire an optimal solution for RL. In this article, the authors propose a new algorithm, SPG-NM, that addresses this issue by incorporating a novel technique called negative momentum (NM).
SPG-NM builds upon the classical SPG algorithm, but with the addition of NM. What makes this algorithm stand out is its unique approach to applying NM. Unlike existing techniques, SPG-NM utilizes a few hyper-parameters to optimize the performance. This distinction sets it apart from other algorithms in terms of computational complexity as well. SPG-NM performs at a similar level to modern SPG-type algorithms such as accelerated policy gradient (APG), which incorporates Nesterov’s accelerated gradient (NAG).
To evaluate the effectiveness of SPG-NM, the authors conducted experiments on two classical tasks: the bandit setting and Markov decision process (MDP). The results clearly demonstrate that SPG-NM achieves a faster convergence rate compared to state-of-the-art algorithms. This highlights the positive impact of NM in accelerating SPG for RL.
Furthermore, the authors conducted numerical experiments under different settings to assess the robustness of SPG-NM. The results confirm that the algorithm remains effective across different scenarios and certain crucial hyper-parameters. This finding is significant as it increases confidence in the practical application of SPG-NM.
Overall, this work presents a novel approach to accelerating the optimization process in reinforcement learning. By incorporating negative momentum into the stochastic policy gradient algorithm, SPG-NM demonstrates improved convergence rates and robustness. The findings pave the way for future advancements in RL algorithms and provide practitioners with a new tool for faster and more efficient RL optimization.