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Active Reinforcement Learning over MDPs

arXiv 2108.02323 ↗First posted 5 Aug 2021cs.LGcited by 0

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Abstract (arXiv)

The past decade has seen the rapid development of Reinforcement Learning, which acquires impressive performance with numerous training resources. However, one of the greatest challenges in RL is generalization efficiency (i.e., generalization performance in a unit time). This paper proposes a framework of Active Reinforcement Learning (ARL) over MDPs to improve generalization efficiency in a limited resource by instance selection. Given a number of instances, the algorithm chooses out valuable instances as training sets while training the policy, thereby costing fewer resources. Unlike existing approaches, we attempt to actively select and use training data rather than train on all the given data, thereby costing fewer resources. Furthermore, we introduce a general instance evaluation metrics and selection mechanism into the framework. Experiments results reveal that the proposed framework with Proximal Policy Optimization as policy optimizer can effectively improve generalization efficiency than unselect-ed and unbiased selected methods.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 23 of its 29 references that are in the map. First posted on arXiv 5 Aug 2021.

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23 of 29 references.

References · 23

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11 more
  1. 2020Deep Learning for Procedural Content Generationcited by 73
  2. 2017Towards Generalization and Simplicity in Continuous Controlcited by 68
  3. 2019Increasing Generality in Machine Learning through Procedural Content Generationcited by 59
  4. 2018Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translationcited by 50
  5. 2020Discount Factor as a Regularizer in Reinforcement Learningcited by 45
  6. 2020Invariant Policy Optimization: Towards Stronger Generalization in Reinforcement Learningcited by 37
  7. 2020Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learningcited by 35
  8. 2018StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer Learningcited by 25
  9. 2020Rotation, Translation, and Cropping for Zero-Shot Generalizationcited by 24
  10. 2021When Is Generalizable Reinforcement Learning Tractable?cited by 20
  11. 2018Deep Curiosity Search: Intra-Life Exploration Can Improve Performance on Challenging Deep Reinforcement Learning Problemscited by 12

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