mlsanityData to 7 Oct 2026

Self-Paced Deep Reinforcement Learning

arXiv 2004.11812 ↗First posted 24 Apr 2020cs.LG cs.AI stat.MLcited by 49

Not on the map. Why?

Abstract (arXiv)

Curriculum reinforcement learning (CRL) improves the learning speed and stability of an agent by exposing it to a tailored series of tasks throughout learning. Despite empirical successes, an open question in CRL is how to automatically generate a curriculum for a given reinforcement learning (RL) agent, avoiding manual design. In this paper, we propose an answer by interpreting the curriculum generation as an inference problem, where distributions over tasks are progressively learned to approach the target task. This approach leads to an automatic curriculum generation, whose pace is controlled by the agent, with solid theoretical motivation and easily integrated with deep RL algorithms. In the conducted experiments, the curricula generated with the proposed algorithm significantly improve learning performance across several environments and deep RL algorithms, matching or outperforming state-of-the-art existing CRL algorithms.

Key facts. Cited by 49 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Apr 2020.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 5; 2022: 8; 2023: 11; 2024: 4; 2025: 8; 2026: 13.

49 papers.

Sort:

By area:

  1. 2022Automated Reinforcement Learning (AutoRL): A Survey and Open Problemscited by 69
  2. 2022Contextualize Me -- The Case for Context in Reinforcement Learningcited by 41
  3. 2022Automated Dynamic Algorithm Configurationcited by 37
  4. 2022Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptationcited by 28
  5. 2021Self-Paced Context Evaluation for Contextual Reinforcement Learningcited by 25
  6. 2021Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problemscited by 25
  7. 2021Specializing Versatile Skill Libraries using Local Mixture of Expertscited by 24
  8. 2021CARL: A Benchmark for Contextual and Adaptive Reinforcement Learningcited by 21
  9. 2023Proximal Curriculum for Reinforcement Learning Agentscited by 19
  10. 2021TeachMyAgent: a Benchmark for Automatic Curriculum Learning in Deep RLcited by 18
  11. 2022Understanding the Complexity Gains of Single-Task RL with a Curriculumcited by 16
  12. 2022N2M2: Learning Navigation for Arbitrary Mobile Manipulation Motions in Unseen and Dynamic Environmentscited by 15
37 more
  1. 2023Domain Randomization via Entropy Maximizationcited by 15
  2. 2024Acquiring Diverse Skills using Curriculum Reinforcement Learning with Mixture of Expertscited by 14
  3. 2023Train Hard, Fight Easy: Robust Meta Reinforcement Learningcited by 11
  4. 2022Robust Reinforcement Learning via Genetic Curriculumcited by 10
  5. 2022Monte Carlo Tree Search based Hybrid Optimization of Variational Quantum Circuitscited by 10
  6. 2023Robust Adversarial Reinforcement Learning via Bounded Rationality Curriculacited by 9
  7. 2023Variational Curriculum Reinforcement Learning for Unsupervised Discovery of Skillscited by 8
  8. 2023On the Benefit of Optimal Transport for Curriculum Reinforcement Learningcited by 6
  9. 2024Proximal Curriculum with Task Correlations for Deep Reinforcement Learningcited by 6
  10. 2023Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learningcited by 4
  11. 2023Continual Driving Policy Optimization with Closed-Loop Individualized Curriculacited by 4
  12. 2024Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learningcited by 3
  13. 2025EvoCurr: Self-evolving Curriculum with Behavior Code Generation for Complex Decision-makingcited by 3
  14. 2024Exploring CausalWorld: Enhancing robotic manipulation via knowledge transfer and curriculum learningcited by 2
  15. 2025cMALC-D: Contextual Multi-Agent LLM-Guided Curriculum Learning with Diversity-Based Context Blendingcited by 2
  16. 2025Curriculum Design for Trajectory-Constrained Agent: Compressing Chain-of-Thought Tokens in LLMscited by 2
  17. 2026RL-AWB: Deep Reinforcement Learning for Auto White Balance Correction in Low-Light Night-time Scenescited by 2
  18. 2026Structure Detection for Contextual Reinforcement Learningcited by 2
  19. 2023Reward-Machine-Guided, Self-Paced Reinforcement Learningcited by 1
  20. 2025TRACED: Transition-aware Regret Approximation with Co-learnability for Environment Designcited by 1
  21. 2025Causal-Paced Deep Reinforcement Learningcited by 1
  22. 2025Distributionally Robust Self Paced Curriculum Reinforcement Learningcited by 1
  23. 2026Contextual Intelligence The Next Leap for Reinforcement Learningcited by 1
  24. 2026Co-Evolution in Agentic Systems: Toward Self-Directed Evolution Beyond Human Designcited by 1
  25. 2026Active Curriculum Refinement for Reinforcement Learningcited by 1
  26. 2023Learning Embeddings for Sequential Tasks Using Population of Agentscited by 0
  27. 2023Self-Paced Absolute Learning Progress as a Regularized Approach to Curriculum Learningcited by 0
  28. 2025Autonomous Curriculum Design via Relative Entropy Based Task Modificationscited by 0
  29. 2025Dynamic Obstacle Avoidance with Bounded Rationality Adversarial Reinforcement Learningcited by 0
  30. 2026Self Paced Gaussian Contextual Reinforcement Learningcited by 0
  31. 2026DC-Ada: Reward-Only Decentralized Sensor Adaptation for Heterogeneous Multi-Robot Teamscited by 0
  32. 2026Curriculum reinforcement learning with measurable task representation learningcited by 0
  33. 2026Self-Paced Curriculum Reinforcement Learning for Autonomous Superbike Racing in Simulationcited by 0
  34. 2026SCOUT: Per-Context Reset Curricula for Sparse-Reward Reinforcement Learningcited by 0
  35. 2026Curriculum Generation under Structured Parametric Environments for Robust Navigation Policiescited by 0
  36. 2026To Go Far, Go Together: Diverse Preferences Induce a Curriculum for Reward Optimizationcited by 0
  37. 2026Dynamic Context Scheduling: Learning Beyond the Static Universecited by 0