From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning

• URL: http://arxiv.org/abs/2501.17842v1
• Date: Wed, 29 Jan 2025 18:46:35 GMT
• Title: From Sparse to Dense: Toddler-inspired Reward Transition in Goal-Oriented Reinforcement Learning
• Abstract summary: Reinforcement learning (RL) agents often face challenges in balancing exploration and exploitation.<n>Our study focuses on transitioning from sparse to potential-based dense (S2D) rewards while preserving optimal strategies.
• Score: 17.230478797343963
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Reinforcement learning (RL) agents often face challenges in balancing exploration and exploitation, particularly in environments where sparse or dense rewards bias learning. Biological systems, such as human toddlers, naturally navigate this balance by transitioning from free exploration with sparse rewards to goal-directed behavior guided by increasingly dense rewards. Inspired by this natural progression, we investigate the Toddler-Inspired Reward Transition in goal-oriented RL tasks. Our study focuses on transitioning from sparse to potential-based dense (S2D) rewards while preserving optimal strategies. Through experiments on dynamic robotic arm manipulation and egocentric 3D navigation tasks, we demonstrate that effective S2D reward transitions significantly enhance learning performance and sample efficiency. Additionally, using a Cross-Density Visualizer, we show that S2D transitions smooth the policy loss landscape, resulting in wider minima that improve generalization in RL models. In addition, we reinterpret Tolman's maze experiments, underscoring the critical role of early free exploratory learning in the context of S2D rewards.

Related papers

• Stage-Transition Dense Reward Modeling for Reinforcement Learning [22.634812963829194]
  Reinforcement learning for long-horizon robotic manipulation is often limited by sparse and delayed rewards.<n>This work proposes Stage-Transition Dense Reward (STDR), a visual reward-learning framework that converts unstructured expert videos into logically grounded dense rewards for training RL agents from scratch.<n>STDR delivers two complementary learning signals during online training: (i) stage-transition feedback that provides goal-directed reward, and (ii) within-stage progress feedback that supplies fine-grained guidance toward completing each stage.
  arXiv  Detail & Related papers  (2026-06-30T09:07:19Z)
• Fuzzy Logic Theory-based Adaptive Reward Shaping for Robust Reinforcement Learning (FARS) [2.8055179094637683]
  Reinforcement learning (RL) often struggles in real-world tasks with high-dimensional state spaces and long horizons.<n>This paper presents a fuzzy logic based reward shaping method that integrates human intuition into RL reward design.
  arXiv  Detail & Related papers  (2026-04-17T07:25:55Z)
• Match or Replay: Self Imitating Proximal Policy Optimization [14.033176618356746]
  Reinforcement Learning (RL) agents often struggle with inefficient exploration.<n>Traditional exploration strategies can lead to slow learning and suboptimal performance.<n>We propose a self-imitating on-policy algorithm that enhances exploration and sample efficiency.
  arXiv  Detail & Related papers  (2026-03-29T04:44:48Z)
• Towards better dense rewards in Reinforcement Learning Applications [0.7344790292816897]
  Finding meaningful and accurate dense rewards is a fundamental task in the field of reinforcement learning (RL)<n>Dense reward functions offer a potential solution by shaping agent behavior and accelerating learning.<n>This proposal explores several approaches to dealing with unsolved problems and enhancing the effectiveness and reliability of dense reward construction.
  arXiv  Detail & Related papers  (2025-12-03T22:36:55Z)
• Spatial-SSRL: Enhancing Spatial Understanding via Self-Supervised Reinforcement Learning [93.19037653970622]
  We introduce Spatial-SSRL, a self-supervised RL paradigm that derives verifiable signals directly from ordinary RGB or RGB-D images.<n>Training on our tasks substantially improves spatial reasoning while preserving general visual capabilities.<n>Our results show that simple, intrinsic supervision enables RLVR at scale and provides a practical route to stronger spatial intelligence in LVLMs.
  arXiv  Detail & Related papers  (2025-10-31T16:30:08Z)
• Navigate the Unknown: Enhancing LLM Reasoning with Intrinsic Motivation Guided Exploration [33.807927649100805]
  Reinforcement learning (RL) has emerged as a pivotal method for improving the reasoning capabilities of Large Language Models (LLMs)<n>RL approaches face critical limitations due to their reliance on sparse outcome-based rewards and inadequate mechanisms for incentivizing exploration.<n>We propose an Intrinsic Motivation guidEd exploratioN meThOd foR LLM Reasoning (i-MENTOR)<n>i-MENTOR introduces three key innovations: trajectory-aware exploration rewards that mitigate bias in token-level strategies; dynamic reward scaling to stabilize exploration and exploitation in large action spaces; and advantage-preserving reward implementation that maintains
  arXiv  Detail & Related papers  (2025-05-23T08:30:28Z)
• 1000 Layer Networks for Self-Supervised RL: Scaling Depth Can Enable New Goal-Reaching Capabilities [18.997261006638798]
  Scaling up self-supervised learning has driven breakthroughs in language and vision, yet comparable progress has remained elusive in reinforcement learning (RL) In this paper, we study building blocks for self-supervised RL that unlock substantial improvements in scalability, with network depth serving as a critical factor.
  arXiv  Detail & Related papers  (2025-03-19T03:33:57Z)
• Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks [2.1040342571709885]
  In this work, inspired by intrinsic motivation theory, we postulate that the intrinsic stimuli of novelty and surprise can assist in improving exploration in complex, sparsely rewarded environments. We introduce a novel sample-efficient method able to learn directly from pixels, an image-based extension of TD3 with an autoencoder called textitNaSA-TD3. Experiments demonstrate that NaSA-TD3 is easy to train and an efficient method for tackling complex continuous-control robotic tasks, both in simulated environments and real-world settings.
  arXiv  Detail & Related papers  (2024-07-31T05:11:06Z)
• 4D Contrastive Superflows are Dense 3D Representation Learners [62.433137130087445]
  We introduce SuperFlow, a novel framework designed to harness consecutive LiDAR-camera pairs for establishing pretraining objectives. To further boost learning efficiency, we incorporate a plug-and-play view consistency module that enhances alignment of the knowledge distilled from camera views.
  arXiv  Detail & Related papers  (2024-07-08T17:59:54Z)
• Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning [16.93475375389869]
  Drawing inspiration from this Toddler-Inspired Reward Transition, we set out to explore the implications of varying reward transitions when incorporated into Reinforcement Learning (RL) tasks. Through various experiments, including those in egocentric navigation and robotic arm manipulation tasks, we found that proper reward transitions significantly influence sample efficiency and success rates.
  arXiv  Detail & Related papers  (2024-03-11T16:34:23Z)
• DreamSmooth: Improving Model-based Reinforcement Learning via Reward Smoothing [60.21269454707625]
  DreamSmooth learns to predict a temporally-smoothed reward, instead of the exact reward at the given timestep. We show that DreamSmooth achieves state-of-the-art performance on long-horizon sparse-reward tasks.
  arXiv  Detail & Related papers  (2023-11-02T17:57:38Z)
• Fantastic Rewards and How to Tame Them: A Case Study on Reward Learning for Task-oriented Dialogue Systems [111.80916118530398]
  reinforcement learning (RL) techniques can naturally be utilized to train dialogue strategies to achieve user-specific goals. This paper aims at answering the question of how to efficiently learn and leverage a reward function for training end-to-end (E2E) ToD agents.
  arXiv  Detail & Related papers  (2023-02-20T22:10:04Z)
• SSMTL++: Revisiting Self-Supervised Multi-Task Learning for Video Anomaly Detection [108.57862846523858]
  We revisit the self-supervised multi-task learning framework, proposing several updates to the original method. We modernize the 3D convolutional backbone by introducing multi-head self-attention modules. In our attempt to further improve the model, we study additional self-supervised learning tasks, such as predicting segmentation maps.
  arXiv  Detail & Related papers  (2022-07-16T19:25:41Z)
• Dealing with Sparse Rewards Using Graph Neural Networks [0.15540058359482856]
  We propose two modifications of one of the recent reward shaping methods based on graph convolutional networks. We empirically validate the effectiveness of our solutions for the task of navigation in a 3D environment with sparse rewards. For the solution featuring attention mechanism, we are also able to show that the learned attention is concentrated on edges corresponding to important transitions in 3D environment.
  arXiv  Detail & Related papers  (2022-03-25T02:42:07Z)
• PEBBLE: Feedback-Efficient Interactive Reinforcement Learning via Relabeling Experience and Unsupervised Pre-training [94.87393610927812]
  We present an off-policy, interactive reinforcement learning algorithm that capitalizes on the strengths of both feedback and off-policy learning. We demonstrate that our approach is capable of learning tasks of higher complexity than previously considered by human-in-the-loop methods.
  arXiv  Detail & Related papers  (2021-06-09T14:10:50Z)
• Hierarchical Reinforcement Learning in StarCraft II with Human Expertise in Subgoals Selection [13.136763521789307]
  We propose a new method to integrate HRL, experience replay and effective subgoal selection through an implicit curriculum design based on human expertise. Our method can achieve better sample efficiency than flat and end-to-end RL methods, and provides an effective method for explaining the agent's performance.
  arXiv  Detail & Related papers  (2020-08-08T04:56:30Z)
• Bridging the Imitation Gap by Adaptive Insubordination [88.35564081175642]
  We show that when the teaching agent makes decisions with access to privileged information, this information is marginalized during imitation learning. We propose 'Adaptive Insubordination' (ADVISOR) to address this gap. ADVISOR dynamically weights imitation and reward-based reinforcement learning losses during training, enabling on-the-fly switching between imitation and exploration.
  arXiv  Detail & Related papers  (2020-07-23T17:59:57Z)

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.