Summary of Etgl-ddpg: a Deep Deterministic Policy Gradient Algorithm For Sparse Reward Continuous Control, by Ehsan Futuhi et al.
ETGL-DDPG: A Deep Deterministic Policy Gradient Algorithm for Sparse Reward Continuous Control
by Ehsan Futuhi, Shayan Karimi, Chao Gao, Martin Müller
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers develop an algorithm for reinforcement learning with sparse rewards using deep deterministic policy gradients (DDPG). To improve exploration, they introduce a search procedure called {}{t}-greedy that generates options for visiting less-explored states. The team also proposes a dual experience replay buffer framework, GDRB, and implements longest n-step returns to efficiently use rewarded transitions. The resulting algorithm, ETGL-DDPG, integrates these techniques into DDPG and outperforms other state-of-the-art methods on standard benchmarks in continuous environments with sparse rewards. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores ways to improve deep learning algorithms for tasks that have sparse rewards. In other words, it’s about finding the best way to learn from rewards when those rewards are only given sometimes. To do this, the authors create a new algorithm called ETGL-DDPG that combines different techniques to help the algorithm explore and learn more efficiently. |
Keywords
* Artificial intelligence * Deep learning * Reinforcement learning
