Summary of From Two-dimensional to Three-dimensional Environment with Q-learning: Modeling Autonomous Navigation with Reinforcement Learning and No Libraries, by Ergon Cugler De Moraes Silva
From Two-Dimensional to Three-Dimensional Environment with Q-Learning: Modeling Autonomous Navigation with Reinforcement Learning and no Libraries
by Ergon Cugler de Moraes Silva
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation (stat.CO)
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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 study investigates the performance of reinforcement learning (RL) agents in both two-dimensional (2D) and three-dimensional (3D) environments. The aim is to explore the dynamics of learning across different spatial dimensions, without relying on pre-made libraries. The methodological framework centers on RL principles, employing a Q-learning agent class and distinct environment classes tailored to each spatial dimension. The study evaluates agents’ learning trajectories and adaptation processes in both 2D and 3D settings, revealing insights into the efficacy of RL algorithms in navigating complex, multi-dimensional spaces. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning helps machines learn by interacting with their surroundings. This research looks at how well these machines can learn when moving from a simple 2D space to a more complicated 3D one. The scientists created an algorithm from scratch, without using any pre-made libraries or tools. They tested this algorithm in both 2D and 3D environments to see how well it could adapt and make decisions. This study helps us understand how machines learn and might even lead to new discoveries about learning in more complex spaces. |
Keywords
* Artificial intelligence * Reinforcement learning
