The intrinsic motivation of reinforcement and imitation learning for sequential tasks

• URL: http://arxiv.org/abs/2412.20573v1
• Date: Sun, 29 Dec 2024 20:44:59 GMT
• Title: The intrinsic motivation of reinforcement and imitation learning for sequential tasks
• Authors: Sao Mai Nguyen,
• Abstract summary: This work aims to devise a new domain bridging between reinforcement learning and imitation learning. We propose a common formulation of intrinsic motivation based on empirical progress for a learning agent to choose automatically its learning curriculum. We developed the framework of socially guided intrinsic motivation with machine learning algorithms to learn multiple tasks.
• Score: 0.5439020425818999
• License:
• Abstract: This work in the field of developmental cognitive robotics aims to devise a new domain bridging between reinforcement learning and imitation learning, with a model of the intrinsic motivation for learning agents to learn with guidance from tutors multiple tasks, including sequential tasks. The main contribution has been to propose a common formulation of intrinsic motivation based on empirical progress for a learning agent to choose automatically its learning curriculum by actively choosing its learning strategy for simple or sequential tasks: which task to learn, between autonomous exploration or imitation learning, between low-level actions or task decomposition, between several tutors. The originality is to design a learner that benefits not only passively from data provided by tutors, but to actively choose when to request tutoring and what and whom to ask. The learner is thus more robust to the quality of the tutoring and learns faster with fewer demonstrations. We developed the framework of socially guided intrinsic motivation with machine learning algorithms to learn multiple tasks by taking advantage of the generalisability properties of human demonstrations in a passive manner or in an active manner through requests of demonstrations from the best tutor for simple and composing subtasks. The latter relies on a representation of subtask composition proposed for a construction process, which should be refined by representations used for observational processes of analysing human movements and activities of daily living. With the outlook of a language-like communication with the tutor, we investigated the emergence of a symbolic representation of the continuous sensorimotor space and of tasks using intrinsic motivation. We proposed within the reinforcement learning framework, a reward function for interacting with tutors for automatic curriculum learning in multi-task learning.

Related papers

• Multi-agent cooperation through learning-aware policy gradients [53.63948041506278]
  Self-interested individuals often fail to cooperate, posing a fundamental challenge for multi-agent learning. We present the first unbiased, higher-derivative-free policy gradient algorithm for learning-aware reinforcement learning. We derive from the iterated prisoner's dilemma a novel explanation for how and when cooperation arises among self-interested learning-aware agents.
  arXiv  Detail & Related papers  (2024-10-24T10:48:42Z)
• RILe: Reinforced Imitation Learning [60.63173816209543]
  RILe is a framework that combines the strengths of imitation learning and inverse reinforcement learning to learn a dense reward function efficiently. Our framework produces high-performing policies in high-dimensional tasks where direct imitation fails to replicate complex behaviors.
  arXiv  Detail & Related papers  (2024-06-12T17:56:31Z)
• Continual Robot Learning using Self-Supervised Task Inference [19.635428830237842]
  We propose a self-supervised task inference approach to continually learn new tasks. We use a behavior-matching self-supervised learning objective to train a novel Task Inference Network (TINet) A multi-task policy is built on top of the TINet and trained with reinforcement learning to optimize performance over tasks.
  arXiv  Detail & Related papers  (2023-09-10T09:32:35Z)
• Incremental procedural and sensorimotor learning in cognitive humanoid robots [52.77024349608834]
  This work presents a cognitive agent that can learn procedures incrementally. We show the cognitive functions required in each substage and how adding new functions helps address tasks previously unsolved by the agent. Results show that this approach is capable of solving complex tasks incrementally.
  arXiv  Detail & Related papers  (2023-04-30T22:51:31Z)
• Saliency-Regularized Deep Multi-Task Learning [7.3810864598379755]
  Multitask learning enforces multiple learning tasks to share knowledge to improve their generalization abilities. Modern deep multitask learning can jointly learn latent features and task sharing, but they are obscure in task relation. This paper proposes a new multitask learning framework that jointly learns latent features and explicit task relations.
  arXiv  Detail & Related papers  (2022-07-03T20:26:44Z)
• Autonomous Open-Ended Learning of Tasks with Non-Stationary Interdependencies [64.0476282000118]
  Intrinsic motivations have proven to generate a task-agnostic signal to properly allocate the training time amongst goals. While the majority of works in the field of intrinsically motivated open-ended learning focus on scenarios where goals are independent from each other, only few of them studied the autonomous acquisition of interdependent tasks. In particular, we first deepen the analysis of a previous system, showing the importance of incorporating information about the relationships between tasks at a higher level of the architecture. Then we introduce H-GRAIL, a new system that extends the previous one by adding a new learning layer to store the autonomously acquired sequences
  arXiv  Detail & Related papers  (2022-05-16T10:43:01Z)
• Human-Centered Prior-Guided and Task-Dependent Multi-Task Representation Learning for Action Recognition Pre-Training [8.571437792425417]
  We propose a novel action recognition pre-training framework, which exploits human-centered prior knowledge that generates more informative representation. Specifically, we distill knowledge from a human parsing model to enrich the semantic capability of representation. In addition, we combine knowledge distillation with contrastive learning to constitute a task-dependent multi-task framework.
  arXiv  Detail & Related papers  (2022-04-27T06:51:31Z)
• Teachable Reinforcement Learning via Advice Distillation [161.43457947665073]
  We propose a new supervision paradigm for interactive learning based on "teachable" decision-making systems that learn from structured advice provided by an external teacher. We show that agents that learn from advice can acquire new skills with significantly less human supervision than standard reinforcement learning algorithms.
  arXiv  Detail & Related papers  (2022-03-19T03:22:57Z)
• 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)
• Intrinsically Motivated Open-Ended Multi-Task Learning Using Transfer Learning to Discover Task Hierarchy [0.0]
  In open-ended continuous environments, robots need to learn multiple parameterised control tasks in hierarchical reinforcement learning. We show that the most complex tasks can be learned more easily by transferring knowledge from simpler tasks, and faster by adapting the complexity of the actions to the task. We propose a task-oriented representation of complex actions, called procedures, to learn online task relationships and unbounded sequences of action primitives to control the different observables of the environment.
  arXiv  Detail & Related papers  (2021-02-19T10:44:08Z)
• Behavior Priors for Efficient Reinforcement Learning [97.81587970962232]
  We consider how information and architectural constraints can be combined with ideas from the probabilistic modeling literature to learn behavior priors. We discuss how such latent variable formulations connect to related work on hierarchical reinforcement learning (HRL) and mutual information and curiosity based objectives. We demonstrate the effectiveness of our framework by applying it to a range of simulated continuous control domains.
  arXiv  Detail & Related papers  (2020-10-27T13:17:18Z)

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.