Summary of Optimal Policy Sparsification and Low Rank Decomposition For Deep Reinforcement Learning, by Vikram Goddla
Optimal Policy Sparsification and Low Rank Decomposition for Deep Reinforcement Learning
by Vikram Goddla
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed novel L0-norm-regularization technique uses an optimal sparsity map to sparsify Deep Reinforcement Learning (DRL) policies, promoting decomposition to a lower rank without decay in rewards. This approach is designed to overcome the limitations of dense DRL policies, which consume extraordinary computing resources and are prone to overfitting. The technique was evaluated across five different environments, including Cartpole-v1, Acrobat-v1, LunarLander-v2, SuperMarioBros-7.1.v0, and Surgical Robot Learning. Results show significant sparsity and compression gains, with the L0-norm-regularized DRL policy in the SuperMarioBros environment achieving 93% sparsity and gaining 70% compression when decomposed to a lower rank. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Deep reinforcement learning is a powerful tool that can be used for many different tasks, such as playing video games or controlling robots. However, training these models requires a lot of computer power and memory, which can make it difficult to use them in real-world applications. One way to solve this problem is by making the models smaller and more efficient. This paper proposes a new method for doing this, called L0-norm-regularization. The authors tested their method on five different environments and found that it was very effective at reducing the size of the models while still allowing them to perform well. |
Keywords
* Artificial intelligence * Overfitting * Regularization * Reinforcement learning
