mlsanityData to 14 Oct 2026

Boosting Reinforcement Learning in 3D Visuospatial Tasks Through Human-Informed Curriculum Design

arXiv 2511.17595 ↗First posted 17 Nov 2025cs.LG cs.AIcited by 0

On the map: Offline RL and imitation learning from data → Imitation learning methods

Abstract (arXiv)

Reinforcement Learning is a mature technology, often suggested as a potential route towards Artificial General Intelligence, with the ambitious goal of replicating the wide range of abilities found in natural and artificial intelligence, including the complexities of human cognition. While RL had shown successes in relatively constrained environments, such as the classic Atari games and specific continuous control problems, recent years have seen efforts to expand its applicability. This work investigates the potential of RL in demonstrating intelligent behaviour and its progress in addressing more complex and less structured problem domains. We present an investigation into the capacity of modern RL frameworks in addressing a seemingly straightforward 3D Same-Different visuospatial task. While initial applications of state-of-the-art methods, including PPO, behavioural cloning and imitation learning, revealed challenges in directly learning optimal strategies, the successful implementation of curriculum learning offers a promising avenue. Effective learning was achieved by strategically designing the lesson plan based on the findings of a real-world human experiment.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data. Builds on 11 of its 30 references that are in the map. First posted on arXiv 17 Nov 2025.

Builds on

11 of 30 references.

Closest ancestors · 1

Same direction, newest first

  1. 2019Go-Explore: a New Approach for Hard-Exploration Problems30 Jan

Other references · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2022Generative Adversarial Networkscited by 5,627
  3. 2017Deep reinforcement learning from human preferencescited by 3,912
  4. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  5. 2019Solving Rubik's Cube with a Robot Handcited by 734
  6. 2018Unity: A General Platform for Intelligent Agentscited by 236
  7. 2020What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Studycited by 162
  8. 2020PESAO: Psychophysical Experimental Setup for Active Observerscited by 4
  9. 2021Blocks World Revisited: The Effect of Self-Occlusion on Classification by Convolutional Neural Networkscited by 4
  10. 2023The Psychophysics of Human Three-Dimensional Active Visuospatial Problem-Solvingcited by 2

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  1. 2025Scalable Multi-Task Reinforcement Learning for Generalizable Spatial Intelligence in Visuomotor Agents
  2. 2022Teacher-student curriculum learning for reinforcement learning
  3. 2025Probabilistic Curriculum Learning for Goal-Based Reinforcement Learning
  4. 2024Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research
  5. 2024Syllabus: Portable Curricula for Reinforcement Learning Agents
  6. 2021Improving Reinforcement Learning with Human Assistance: An Argument for Human Subject Studies with HIPPO Gym
  7. 2022MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations
  8. 2023A Survey of Reinforcement Learning from Human Feedback