Meta-Q-LearningDownload PDF

Sep 25, 2019 (edited Feb 10, 2022)ICLR 2020 Conference Blind SubmissionReaders: Everyone
  • Original Pdf: pdf
  • Code: [![github](/images/github_icon.svg) amazon-research/meta-q-learning](https://github.com/amazon-research/meta-q-learning)
  • Data: [OpenAI Gym](https://paperswithcode.com/dataset/openai-gym)
  • Keywords: meta reinforcement learning, propensity estimation, off-policy
  • TL;DR: MQL is a simple off-policy meta-RL algorithm that recycles data from the meta-training replay buffer to adapt to new tasks.
  • Abstract: 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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