Summary of Drama: Mamba-enabled Model-based Reinforcement Learning Is Sample and Parameter Efficient, by Wenlong Wang et al.
Drama: Mamba-Enabled Model-Based Reinforcement Learning Is Sample and Parameter Efficient
by Wenlong Wang, Ivana Dusparic, Yucheng Shi, Ke Zhang, Vinny Cahill
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 In this paper, researchers tackle the challenge of model-based reinforcement learning (RL) by developing a novel approach to learn robust world models. The proposed method addresses the data inefficiency issue that plagues most model-free RL algorithms and relies on complex and deep architectures for accurate predictions. Specifically, the study explores various dynamics-model architectures, including recurrent neural network (RNN) based and transformer-based models. The authors highlight the limitations of current RNN-based approaches, such as vanishing gradients and difficulty in capturing long-term dependencies effectively. In contrast, transformer-based models face challenges related to self-attention mechanisms, which scale quadratically with sequence length. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us learn better about the world by creating more accurate models that can predict what will happen next. Right now, many of these models are very complex and take a long time to train because they try to understand everything all at once. The researchers in this study want to make it easier to create these models so we can use them for things like helping robots learn new tasks or making computers better at playing games. |
Keywords
* Artificial intelligence * Neural network * Reinforcement learning * Rnn * Self attention * Transformer
