mlsanityData to 7 Oct 2026

Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution

arXiv 2009.14108 ↗First posted 29 Sep 2020cs.LG cs.AI stat.MLcited by 35

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Abstract (arXiv)

Reinforcement learning algorithms require many samples when solving complex hierarchical tasks with sparse and delayed rewards. For such complex tasks, the recently proposed RUDDER uses reward redistribution to leverage steps in the Q-function that are associated with accomplishing sub-tasks. However, often only few episodes with high rewards are available as demonstrations since current exploration strategies cannot discover them in reasonable time. In this work, we introduce Align-RUDDER, which utilizes a profile model for reward redistribution that is obtained from multiple sequence alignment of demonstrations. Consequently, Align-RUDDER employs reward redistribution effectively and, thereby, drastically improves learning on few demonstrations. Align-RUDDER outperforms competitors on complex artificial tasks with delayed rewards and few demonstrations. On the Minecraft ObtainDiamond task, Align-RUDDER is able to mine a diamond, though not frequently. Code is available at https://github.com/ml-jku/align-rudder. YouTube: https://youtu.be/HO-_8ZUl-UY

Key facts. Cited by 35 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Sep 2020.

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Citing papers per year: 2021: 3; 2022: 8; 2023: 10; 2024: 11; 2025: 2; 2026: 1.

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  10. 2024Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement Learningcited by 22
  11. 2024Retrieval-Augmented Decision Transformer: External Memory for In-context RLcited by 20
  12. 2023Interpretable Reward Redistribution in Reinforcement Learning: A Causal Approachcited by 17
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  5. 2024Hindsight PRIORs for Reward Learning from Human Preferencescited by 12
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  7. 2022Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement Learningcited by 9
  8. 2023Off-Policy Evaluation for Human Feedbackcited by 6
  9. 2023STAS: Spatial-Temporal Return Decomposition for Multi-agent Reinforcement Learningcited by 5
  10. 2025Attention-Based Reward Shaping for Sparse and Delayed Rewardscited by 5
  11. 2022GriddlyJS: A Web IDE for Reinforcement Learningcited by 4
  12. 2022Contrastive Retrospection: honing in on critical steps for rapid learning and generalization in RLcited by 4
  13. 2021Distilling Reinforcement Learning Tricks for Video Gamescited by 3
  14. 2022Towards Artificial Virtuous Agents: Games, Dilemmas and Machine Learningcited by 3
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  17. 2025Preference-Guided Learning for Sparse-Reward Multi-Agent Reinforcement Learningcited by 2
  18. 2022A Globally Convergent Evolutionary Strategy for Stochastic Constrained Optimization with Applications to Reinforcement Learningcited by 1
  19. 2024Dynamic Policy Fusion for User Alignment Without Re-Interactioncited by 1
  20. 2024Simplified priors for Object-Centric Learningcited by 1
  21. 2024Agent-Temporal Credit Assignment for Optimal Policy Preservation in Sparse Multi-Agent Reinforcement Learningcited by 1
  22. 2026Temporal GRPO: Beyond Trajectory-Level Credit in Vision-Language-Action Reinforcement Learningcited by 1
  23. 2024Reinforcement Learning from Bagged Rewardcited by 0