Summary of Interpretable End-to-end Neurosymbolic Reinforcement Learning Agents, by Nils Grandien et al.
Interpretable end-to-end Neurosymbolic Reinforcement Learning agents
by Nils Grandien, Quentin Delfosse, Kristian Kersting
First submitted to arxiv on: 18 Oct 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 A novel approach is proposed to improve deep reinforcement learning (RL) agents’ ability to generalize to new environments by developing a symbolic method that uses object-centric states. This method, called SCoBots, decomposes RL tasks into intermediate representations and makes decisions based on object-centric relational concepts, making it easier to understand agent decisions. By learning object-centric representations from raw states, the framework blends neural networks with symbolic AI, placing itself within the neurosymbolic AI paradigm. The proposed architecture is evaluated separately on different Atari games, demonstrating its potential to create interpretable and performing RL systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this study, researchers created a new way for artificial intelligence (AI) agents to learn from environments by using object-centric states. This means that instead of just looking at raw pixels, the AI can understand what’s happening in the environment and make decisions based on that understanding. The new approach is called SCoBots and it’s like a middle ground between traditional AI and neural networks. The researchers tested their idea on different video games and found that it works well and can even be used to create more understandable AI agents. |
Keywords
» Artificial intelligence » Reinforcement learning
