Summary of Image-based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: on the Execution Of Complex Robotic Tasks, by David Valencia et al.
Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks
by David Valencia, Henry Williams, Yuning Xing, Trevor Gee, Minas Liarokapis, Bruce A. MacDonald
First submitted to arxiv on: 31 Jul 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG)
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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 This paper explores the application of reinforcement learning (RL) in complex, sparsely rewarded environments where traditional dense reward values are not available. The authors postulate that intrinsic stimuli such as novelty and surprise can aid in improving exploration strategies, leading to more efficient learning processes. To achieve this, they introduce NaSA-TD3, an image-based extension of the TD3 algorithm with an autoencoder, which learns directly from pixels. The method is shown to be sample-efficient and effective for tackling complex continuous-control robotic tasks both in simulated environments and real-world settings, outperforming existing state-of-the-art RL image-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a new way of teaching machines to learn by exploring their environment. Traditionally, rewards are given when something good happens, but sometimes these rewards can be rare or hard to define. The authors think that the machine’s natural curiosity and desire for novelty and surprise can help it learn more efficiently. They created a new method called NaSA-TD3 that uses images and an autoencoder to learn from its environment. This method is able to solve complex tasks, like controlling robots, without needing pre-trained models or human guidance. |
Keywords
* Artificial intelligence * Autoencoder * Reinforcement learning
