Machine Learning Street Talk (MLST) Can we build a generalist agent? Dr. Minqi Jiang and Dr. Marc Rigter
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Mar 20, 2024 Dr. Minqi Jiang, a researcher in reinforcement learning, and Dr. Marc Rigter, an expert in general-purpose agents, discuss groundbreaking strategies for developing versatile AI agents. They delve into their innovative paper on reward-free curricula, which enhances agents' adaptability through diverse world training. The duo explores challenges in curriculum learning, the importance of effective reward functions, and the balance between creativity and model precision. Their insights pave the way for agents that can excel across various environments, redefining AI's potential.
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General Agent Definition
- General agents should perform various tasks in diverse environments.
- This paper argues for achieving this through a general world model.
Curriculum Learning and POET Paper
- Curriculum learning, like Kenneth Stanley's POET paper, involves principled training data selection and ordering.
- This approach improves learner performance by presenting data effectively.
Robustness through Minimax Regret
- Robustness is crucial for general agents and can be achieved by optimizing for minimax regret.
- Minimax regret minimizes the maximum suboptimality across all possible situations.