Summary of Graceful Task Adaptation with a Bi-hemispheric Rl Agent, by Grant Nicholas et al.
Graceful task adaptation with a bi-hemispheric RL agent
by Grant Nicholas, Levin Kuhlmann, Gideon Kowadlo
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 reinforcement learning agent with specialized hemispheres is proposed, inspired by the Novelty-Routine Hypothesis (NRH). The agent uses generalist knowledge from its right hemisphere to avoid poor initial performance on novel tasks. In addition, it maintains the ability to learn novel tasks. The design has minimal impact on the agent’s capability to perform routine tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a new approach, researchers have created an artificial intelligence that can solve problems in a way similar to how humans learn. When faced with a task, our brain’s right hemisphere helps us figure it out at first. As we get better at it, our left hemisphere takes over and makes the process more efficient. The AI agent developed by these scientists mimics this process by having two “hemispheres” that work together to solve problems. This allows the agent to learn from its mistakes on new tasks and still perform well on familiar ones. |
Keywords
* Artificial intelligence * Reinforcement learning
