Summary of Novelgym: a Flexible Ecosystem For Hybrid Planning and Learning Agents Designed For Open Worlds, by Shivam Goel et al.
NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds
by Shivam Goel, Yichen Wei, Panagiotis Lymperopoulos, Klara Chura, Matthias Scheutz, Jivko Sinapov
First submitted to arxiv on: 7 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 introduces NovelGym, a novel ecosystem designed to simulate gridworld environments, enabling the benchmarking of reinforcement learning (RL) and hybrid planning and learning agents in open-world contexts. The platform’s modular architecture allows for rapid creation and modification of task environments, including multi-agent scenarios with multiple environment transformations. This provides a dynamic testbed for researchers to develop AI agents that can operate effectively in real-world scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI robots are getting smarter! But how do we make sure they can handle unexpected situations? That’s where NovelGym comes in – a special computer program that lets scientists test and compare different ways of making decisions. It’s like a big video game, but instead of playing characters, scientists use it to train AI agents to be more flexible and adaptable. This is important because robots will soon be doing lots of things on their own, like driving cars or cooking meals. |
Keywords
» Artificial intelligence » Reinforcement learning
