Boosting Reinforcement Learning in 3D Visuospatial Tasks Through Human-Informed Curriculum Design
- URL: http://arxiv.org/abs/2511.17595v1
- Date: Mon, 17 Nov 2025 18:28:07 GMT
- Title: Boosting Reinforcement Learning in 3D Visuospatial Tasks Through Human-Informed Curriculum Design
- Authors: Markus D. Solbach, John K. Tsotsos,
- Abstract summary: This work investigates the potential of RL in demonstrating intelligent behaviour and its progress in addressing more complex and less structured problem domains.<n>We present an investigation into the capacity of modern RL frameworks in addressing a seemingly straightforward 3D Same-Different visuospatial task.<n>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.
- Score: 1.9766522384767218
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 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.
Related papers
- Curriculum-Based Multi-Tier Semantic Exploration via Deep Reinforcement Learning [1.8374319565577155]
This paper presents a novel Deep Reinforcement Learning architecture that is specifically designed for resource efficient semantic exploration.<n>A key methodological contribution is the integration of a Vision-Language Model (VLM) common-sense through a layered reward function.<n>We show that our agent achieves significantly enhanced object discovery rates and develops a learned capability to effectively navigate towards semantically rich regions.
arXiv Detail & Related papers (2025-09-11T11:10:08Z) - Autonomous Learning From Success and Failure: Goal-Conditioned Supervised Learning with Negative Feedback [2.36462256498849]
Goal-Conditioned Supervised Learning has emerged as a potential solution by enabling self-imitation learning for autonomous systems.<n>We propose a novel model that integrates contrastive learning principles into the GCSL framework to learn from both success and failure.
arXiv Detail & Related papers (2025-09-03T10:50:48Z) - Parental Guidance: Efficient Lifelong Learning through Evolutionary Distillation [1.124958340749622]
We propose a framework that includes a reproduction module, similar to natural species reproduction, balancing diversity and specialization.<n>By integrating RL, imitation learning (IL), and a coevolutionary agent-terrain curriculum, our system evolves agents continuously through complex tasks.<n>Our initial experiments show that this method improves exploration efficiency and supports open-ended learning.
arXiv Detail & Related papers (2025-03-24T10:40:03Z) - O1 Replication Journey: A Strategic Progress Report -- Part 1 [52.062216849476776]
This paper introduces a pioneering approach to artificial intelligence research, embodied in our O1 Replication Journey.
Our methodology addresses critical challenges in modern AI research, including the insularity of prolonged team-based projects.
We propose the journey learning paradigm, which encourages models to learn not just shortcuts, but the complete exploration process.
arXiv Detail & Related papers (2024-10-08T15:13:01Z) - Empowering Large Language Model Agents through Action Learning [85.39581419680755]
Large Language Model (LLM) Agents have recently garnered increasing interest yet they are limited in their ability to learn from trial and error.
We argue that the capacity to learn new actions from experience is fundamental to the advancement of learning in LLM agents.
We introduce a framework LearnAct with an iterative learning strategy to create and improve actions in the form of Python functions.
arXiv Detail & Related papers (2024-02-24T13:13:04Z) - Continual Visual Reinforcement Learning with A Life-Long World Model [55.05017177980985]
We present a new continual learning approach for visual dynamics modeling.<n>We first introduce the life-long world model, which learns task-specific latent dynamics.<n>Then, we address the value estimation challenge for previous tasks with the exploratory-conservative behavior learning approach.
arXiv Detail & Related papers (2023-03-12T05:08:03Z) - Human-Timescale Adaptation in an Open-Ended Task Space [56.55530165036327]
We show that training an RL agent at scale leads to a general in-context learning algorithm that can adapt to open-ended novel embodied 3D problems as quickly as humans.
Our results lay the foundation for increasingly general and adaptive RL agents that perform well across ever-larger open-ended domains.
arXiv Detail & Related papers (2023-01-18T15:39:21Z) - Autonomous Reinforcement Learning: Formalism and Benchmarking [106.25788536376007]
Real-world embodied learning, such as that performed by humans and animals, is situated in a continual, non-episodic world.
Common benchmark tasks in RL are episodic, with the environment resetting between trials to provide the agent with multiple attempts.
This discrepancy presents a major challenge when attempting to take RL algorithms developed for episodic simulated environments and run them on real-world platforms.
arXiv Detail & Related papers (2021-12-17T16:28:06Z) - Procedure Planning in Instructional Videosvia Contextual Modeling and
Model-based Policy Learning [114.1830997893756]
This work focuses on learning a model to plan goal-directed actions in real-life videos.
We propose novel algorithms to model human behaviors through Bayesian Inference and model-based Imitation Learning.
arXiv Detail & Related papers (2021-10-05T01:06:53Z) - Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short
Survey [21.311739361361717]
Developmental approaches argue that learning agents must generate, select and learn to solve their own problems.
Recent years have seen a convergence of developmental approaches and deep reinforcement learning (RL) methods, forming the new domain of developmental machine learning.
This paper proposes a typology of these methods at the intersection of deep RL and developmental approaches, surveys recent approaches and discusses future avenues.
arXiv Detail & Related papers (2020-12-17T18:51:40Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.