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Meta-Q-Learning

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arxiv 1910.00125 v2 pith:6ERMVMRE submitted 2019-09-30 cs.LG stat.ML

Meta-Q-Learning

Rasool Fakoor , Pratik Chaudhari , Stefano Soatto , Alexander J. Smola This is my paper
classification cs.LG stat.ML
keywords meta-rldataideasmeta-q-learningoff-policypastuponaccess
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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This paper introduces Meta-Q-Learning (MQL), a new off-policy algorithm for meta-Reinforcement Learning (meta-RL). MQL builds upon three simple ideas. First, we show that Q-learning is competitive with state-of-the-art meta-RL algorithms if given access to a context variable that is a representation of the past trajectory. Second, a multi-task objective to maximize the average reward across the training tasks is an effective method to meta-train RL policies. Third, past data from the meta-training replay buffer can be recycled to adapt the policy on a new task using off-policy updates. MQL draws upon ideas in propensity estimation to do so and thereby amplifies the amount of available data for adaptation. Experiments on standard continuous-control benchmarks suggest that MQL compares favorably with the state of the art in meta-RL.

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Cited by 4 Pith papers

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  3. The Rise and Potential of Large Language Model Based Agents: A Survey

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    The paper surveys the origins, frameworks, applications, and open challenges of AI agents built on large language models.

  4. Meta-Learning and Meta-Reinforcement Learning -- Tracing the Path towards DeepMind's Adaptive Agent

    cs.AI 2026-02 unverdicted novelty 2.0

    A survey provides a task-based formalization of meta-learning and meta-RL while chronicling algorithms that lead to DeepMind's Adaptive Agent.