Summary of Fast Stochastic Policy Gradient: Negative Momentum For Reinforcement Learning, by Haobin Zhang and Zhuang Yang
Fast Stochastic Policy Gradient: Negative Momentum for Reinforcement Learning
by Haobin Zhang, Zhuang Yang
First submitted to arxiv on: 8 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper presents a novel stochastic policy gradient (SPG) algorithm, dubbed SPG-NM, that accelerates the convergence rate of reinforcement learning (RL) problems. By incorporating negative momentum (NM) into the classical SPG framework, the proposed algorithm outperforms state-of-the-art methods such as accelerated policy gradient (APG) and Nesterov’s accelerated gradient (NAG). The SPG-NM algorithm is evaluated on two classic RL tasks: bandit setting and Markov decision process (MDP), demonstrating faster convergence rates compared to existing algorithms. This work contributes to the development of efficient RL methods, showcasing the positive impact of NM in accelerating SPG for RL applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research aims to make reinforcement learning (RL) more efficient. The team developed a new algorithm called SPG-NM that helps find the best solution faster. They combined two ideas: policy gradient and negative momentum. This combination allows their algorithm to work better than others in similar tasks. They tested it on two classic RL problems and found that it was much faster than existing methods. |
Keywords
» Artificial intelligence » Reinforcement learning
