Energy Weighted Learning Progress Guided Interleaved Multi-Task Learning

• URL: http://arxiv.org/abs/2504.00707v1
• Date: Tue, 01 Apr 2025 12:15:27 GMT
• Title: Energy Weighted Learning Progress Guided Interleaved Multi-Task Learning
• Authors: Hanne Say, Suzan Ece Ada, Emre Ugur, Erhan Oztop,
• Abstract summary: 'Continual learning' in machine learning aims to learn new information while preserving the previously acquired knowledge.<n>We propose a method that interleaves tasks based on their 'learning progress' and energy consumption.<n>From a machine learning perspective, our approach can be seen as a multi-task learning system.
• Score: 1.5749416770494706
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans can continuously acquire new skills and knowledge by exploiting existing ones for improved learning, without forgetting them. Similarly, 'continual learning' in machine learning aims to learn new information while preserving the previously acquired knowledge. Existing research often overlooks the nature of human learning, where tasks are interleaved due to human choice or environmental constraints. So, almost never do humans master one task before switching to the next. To investigate to what extent human-like learning can benefit the learner, we propose a method that interleaves tasks based on their 'learning progress' and energy consumption. From a machine learning perspective, our approach can be seen as a multi-task learning system that balances learning performance with energy constraints while mimicking ecologically realistic human task learning. To assess the validity of our approach, we consider a robot learning setting in simulation, where the robot learns the effect of its actions in different contexts. The conducted experiments show that our proposed method achieves better performance than sequential task learning and reduces energy consumption for learning the tasks.

Related papers

• SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulation [58.14969377419633]
  We propose spire, a system that decomposes tasks into smaller learning subproblems and second combines imitation and reinforcement learning to maximize their strengths. We find that spire outperforms prior approaches that integrate imitation learning, reinforcement learning, and planning by 35% to 50% in average task performance.
  arXiv  Detail & Related papers  (2024-10-23T17:42:07Z)
• Unsupervised Skill Discovery for Robotic Manipulation through Automatic Task Generation [17.222197596599685]
  We propose a Skill Learning approach that discovers composable behaviors by solving a large number of autonomously generated tasks. Our method learns skills allowing the robot to consistently and robustly interact with objects in its environment. The learned skills can be used to solve a set of unseen manipulation tasks, in simulation as well as on a real robotic platform.
  arXiv  Detail & Related papers  (2024-10-07T09:19:13Z)
• Anti-Retroactive Interference for Lifelong Learning [65.50683752919089]
  We design a paradigm for lifelong learning based on meta-learning and associative mechanism of the brain. It tackles the problem from two aspects: extracting knowledge and memorizing knowledge. It is theoretically analyzed that the proposed learning paradigm can make the models of different tasks converge to the same optimum.
  arXiv  Detail & Related papers  (2022-08-27T09:27:36Z)
• Lifelong Robotic Reinforcement Learning by Retaining Experiences [61.79346922421323]
  Many multi-task reinforcement learning efforts assume the robot can collect data from all tasks at all times. In this work, we study a practical sequential multi-task RL problem motivated by the practical constraints of physical robotic systems. We derive an approach that effectively leverages the data and policies learned for previous tasks to cumulatively grow the robot's skill-set.
  arXiv  Detail & Related papers  (2021-09-19T18:00:51Z)
• Behavior Self-Organization Supports Task Inference for Continual Robot Learning [18.071689266826212]
  We propose a novel approach to continual learning of robotic control tasks. Our approach performs unsupervised learning of behavior embeddings by incrementally self-organizing demonstrated behaviors. Unlike previous approaches, our approach makes no assumptions about task distribution and requires no task exploration to infer tasks.
  arXiv  Detail & Related papers  (2021-07-09T16:37:27Z)
• Discovering Generalizable Skills via Automated Generation of Diverse Tasks [82.16392072211337]
  We propose a method to discover generalizable skills via automated generation of a diverse set of tasks. As opposed to prior work on unsupervised discovery of skills, our method pairs each skill with a unique task produced by a trainable task generator. A task discriminator defined on the robot behaviors in the generated tasks is jointly trained to estimate the evidence lower bound of the diversity objective. The learned skills can then be composed in a hierarchical reinforcement learning algorithm to solve unseen target tasks.
  arXiv  Detail & Related papers  (2021-06-26T03:41:51Z)
• CRIL: Continual Robot Imitation Learning via Generative and Prediction Model [8.896427780114703]
  We study how to realize continual imitation learning ability that empowers robots to continually learn new tasks one by one. We propose a novel trajectory generation model that employs both a generative adversarial network and a dynamics prediction model. Our experiments on both simulation and real world manipulation tasks demonstrate the effectiveness of our method.
  arXiv  Detail & Related papers  (2021-06-17T12:15:57Z)
• Active Hierarchical Imitation and Reinforcement Learning [0.0]
  In this project, we explored different imitation learning algorithms and designed active learning algorithms upon the hierarchical imitation and reinforcement learning framework we have developed. Our experimental results showed that using DAgger and reward-based active learning method can achieve better performance while saving more human efforts physically and mentally during the training process.
  arXiv  Detail & Related papers  (2020-12-14T08:27:27Z)
• Hierarchical Affordance Discovery using Intrinsic Motivation [69.9674326582747]
  We propose an algorithm using intrinsic motivation to guide the learning of affordances for a mobile robot. This algorithm is capable to autonomously discover, learn and adapt interrelated affordances without pre-programmed actions. Once learned, these affordances may be used by the algorithm to plan sequences of actions in order to perform tasks of various difficulties.
  arXiv  Detail & Related papers  (2020-09-23T07:18:21Z)
• Learning Transferable Concepts in Deep Reinforcement Learning [0.7161783472741748]
  We show that learning discrete representations of sensory inputs can provide a high-level abstraction that is common across multiple tasks. In particular, we show that it is possible to learn such representations by self-supervision, following an information theoretic approach. Our method is able to learn concepts in locomotive and optimal control tasks that increase the sample efficiency in both known and unknown tasks.
  arXiv  Detail & Related papers  (2020-05-16T04:45:51Z)
• Scalable Multi-Task Imitation Learning with Autonomous Improvement [159.9406205002599]
  We build an imitation learning system that can continuously improve through autonomous data collection. We leverage the robot's own trials as demonstrations for tasks other than the one that the robot actually attempted. In contrast to prior imitation learning approaches, our method can autonomously collect data with sparse supervision for continuous improvement.
  arXiv  Detail & Related papers  (2020-02-25T18:56:42Z)

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.