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Self-Paced Absolute Learning Progress as a Regularized Approach to Curriculum Learning

arXiv 2306.05769 ↗First posted 9 Jun 2023cs.LGcited by 0

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Abstract (arXiv)

The usability of Reinforcement Learning is restricted by the large computation times it requires. Curriculum Reinforcement Learning speeds up learning by defining a helpful order in which an agent encounters tasks, i.e. from simple to hard. Curricula based on Absolute Learning Progress (ALP) have proven successful in different environments, but waste computation on repeating already learned behaviour in new tasks. We solve this problem by introducing a new regularization method based on Self-Paced (Deep) Learning, called Self-Paced Absolute Learning Progress (SPALP). We evaluate our method in three different environments. Our method achieves performance comparable to original ALP in all cases, and reaches it quicker than ALP in two of them. We illustrate possibilities to further improve the efficiency and performance of SPALP.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 13 of its 20 references that are in the map. First posted on arXiv 9 Jun 2023.

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13 of 20 references.

References · 13

  1. 2018Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actorcited by 4,635
  2. 2013Playing Atari with Deep Reinforcement Learningcited by 3,211
  3. 2018QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulationcited by 730
  4. 2017Learning Complex Dexterous Manipulation with Deep Reinforcement Learning and Demonstrationscited by 685
  5. 2018Learning Dexterous In-Hand Manipulationcited by 643
  6. 2017Automatic Goal Generation for Reinforcement Learning Agentscited by 252
  7. 2017Automated Curriculum Learning for Neural Networkscited by 251
  8. 2017Teacher-Student Curriculum Learningcited by 197
  9. 2019Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environmentscited by 113
  10. 2013Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robotscited by 56
  11. 2020Self-Paced Deep Reinforcement Learningcited by 49
  12. 2019Self-Paced Contextual Reinforcement Learningcited by 26
1 more
  1. 2021A Probabilistic Interpretation of Self-Paced Learning with Applications to Reinforcement Learningcited by 21

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