Meta-Value Learning: a General Framework for Learning with Learning Awareness

• URL: http://arxiv.org/abs/2307.08863v3
• Date: Mon, 11 Dec 2023 16:52:51 GMT
• Title: Meta-Value Learning: a General Framework for Learning with Learning Awareness
• Authors: Tim Cooijmans, Milad Aghajohari, Aaron Courville
• Abstract summary: We propose to judge joint policies by their long-term prospects as measured by the meta-value. We apply a form of Q-learning to the meta-game of optimization, in a way that avoids the need to explicitly represent the continuous action space of policy updates.
• Score: 1.4323566945483497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Gradient-based learning in multi-agent systems is difficult because the gradient derives from a first-order model which does not account for the interaction between agents' learning processes. LOLA (arXiv:1709.04326) accounts for this by differentiating through one step of optimization. We propose to judge joint policies by their long-term prospects as measured by the meta-value, a discounted sum over the returns of future optimization iterates. We apply a form of Q-learning to the meta-game of optimization, in a way that avoids the need to explicitly represent the continuous action space of policy updates. The resulting method, MeVa, is consistent and far-sighted, and does not require REINFORCE estimators. We analyze the behavior of our method on a toy game and compare to prior work on repeated matrix games.

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