Bibliografias temáticas / Sparse Reward

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Literatura científica selecionada sobre o tema "Sparse Reward"

Autor: Grafiati
Publicado: 8 de março de 2025
Última modificação: 31 de julho de 2025

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Artigos de revistas sobre o assunto "Sparse Reward"

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Kong, Yan, Junfeng Wei, and Chih-Hsien Hsia. "Solving Sparse Reward Tasks Using Self-Balancing Exploration and Exploitation." Journal of Internet Technology 26, no. 3 (2025): 293–301. https://doi.org/10.70003/160792642025052603002.

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A core challenge in applying deep reinforcement learning (DRL) to real-world tasks is the sparse reward problem, and shaping reward has been one effective method to solve it. However, due to the enormous state space and sparse rewards in the real world, a large number of useless samples may be generated, leading to reduced sample efficiency and potential local optima. To address this issue, this study proposes a self-balancing method of exploration and development to solve the issue of sparse rewards. Firstly, we shape the reward function according to the evaluated progress, to guide the agent
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Park, Junseok, Yoonsung Kim, Hee bin Yoo, et al. "Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 1 (2024): 592–600. http://dx.doi.org/10.1609/aaai.v38i1.27815.

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Toddlers evolve from free exploration with sparse feedback to exploiting prior experiences for goal-directed learning with denser rewards. 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. Central to our inquiry is the transition from sparse to potential-based dense rewards, which share optimal strategies regardless of reward changes. Through various experiments, including those in egocentric navigation and robotic arm manipulation tasks, we found that
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Xu, Pei, Junge Zhang, Qiyue Yin, Chao Yu, Yaodong Yang, and Kaiqi Huang. "Subspace-Aware Exploration for Sparse-Reward Multi-Agent Tasks." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 11717–25. http://dx.doi.org/10.1609/aaai.v37i10.26384.

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Exploration under sparse rewards is a key challenge for multi-agent reinforcement learning problems. One possible solution to this issue is to exploit inherent task structures for an acceleration of exploration. In this paper, we present a novel exploration approach, which encodes a special structural prior on the reward function into exploration, for sparse-reward multi-agent tasks. Specifically, a novel entropic exploration objective which encodes the structural prior is proposed to accelerate the discovery of rewards. By maximizing the lower bound of this objective, we then propose an algor
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Mguni, David, Taher Jafferjee, Jianhong Wang, et al. "Learning to Shape Rewards Using a Game of Two Partners." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 10 (2023): 11604–12. http://dx.doi.org/10.1609/aaai.v37i10.26371.

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Reward shaping (RS) is a powerful method in reinforcement learning (RL) for overcoming the problem of sparse or uninformative rewards. However, RS typically relies on manually engineered shaping-reward functions whose construc- tion is time-consuming and error-prone. It also requires domain knowledge which runs contrary to the goal of autonomous learning. We introduce Reinforcement Learning Optimising Shaping Algorithm (ROSA), an automated reward shaping framework in which the shaping-reward function is constructed in a Markov game between two agents. A reward-shaping agent (Shaper) uses switc
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Meng, Fanxiao. "Research on Multi-agent Sparse Reward Problem." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 96–103. http://dx.doi.org/10.54097/er0mx710.

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Sparse reward poses a significant challenge in deep reinforcement learning, leading to issues such as low sample utilization, slow agent convergence, and subpar performance of optimal policies. Overcoming these challenges requires tackling the complexity of sparse reward algorithms and addressing the lack of unified understanding. This paper aims to address these issues by introducing the concepts of reinforcement learning and sparse reward, as well as presenting three categories of sparse reward algorithms. Furthermore, the paper conducts an analysis and summary of three key aspects: manual l
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Zuo, Guoyu, Qishen Zhao, Jiahao Lu, and Jiangeng Li. "Efficient hindsight reinforcement learning using demonstrations for robotic tasks with sparse rewards." International Journal of Advanced Robotic Systems 17, no. 1 (2020): 172988141989834. http://dx.doi.org/10.1177/1729881419898342.

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The goal of reinforcement learning is to enable an agent to learn by using rewards. However, some robotic tasks naturally specify with sparse rewards, and manually shaping reward functions is a difficult project. In this article, we propose a general and model-free approach for reinforcement learning to learn robotic tasks with sparse rewards. First, a variant of Hindsight Experience Replay, Curious and Aggressive Hindsight Experience Replay, is proposed to improve the sample efficiency of reinforcement learning methods and avoid the need for complicated reward engineering. Second, based on Tw
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Velasquez, Alvaro, Brett Bissey, Lior Barak, et al. "Dynamic Automaton-Guided Reward Shaping for Monte Carlo Tree Search." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 13 (2021): 12015–23. http://dx.doi.org/10.1609/aaai.v35i13.17427.

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Reinforcement learning and planning have been revolutionized in recent years, due in part to the mass adoption of deep convolutional neural networks and the resurgence of powerful methods to refine decision-making policies. However, the problem of sparse reward signals and their representation remains pervasive in many domains. While various rewardshaping mechanisms and imitation learning approaches have been proposed to mitigate this problem, the use of humanaided artificial rewards introduces human error, sub-optimal behavior, and a greater propensity for reward hacking. In this paper, we mi
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Corazza, Jan, Ivan Gavran, and Daniel Neider. "Reinforcement Learning with Stochastic Reward Machines." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (2022): 6429–36. http://dx.doi.org/10.1609/aaai.v36i6.20594.

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Reward machines are an established tool for dealing with reinforcement learning problems in which rewards are sparse and depend on complex sequences of actions. However, existing algorithms for learning reward machines assume an overly idealized setting where rewards have to be free of noise. To overcome this practical limitation, we introduce a novel type of reward machines, called stochastic reward machines, and an algorithm for learning them. Our algorithm, based on constraint solving, learns minimal stochastic reward machines from the explorations of a reinforcement learning agent. This al
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Gaina, Raluca D., Simon M. Lucas, and Diego Pérez-Liébana. "Tackling Sparse Rewards in Real-Time Games with Statistical Forward Planning Methods." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 1691–98. http://dx.doi.org/10.1609/aaai.v33i01.33011691.

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One of the issues general AI game players are required to deal with is the different reward systems in the variety of games they are expected to be able to play at a high level. Some games may present plentiful rewards which the agents can use to guide their search for the best solution, whereas others feature sparse reward landscapes that provide little information to the agents. The work presented in this paper focuses on the latter case, which most agents struggle with. Thus, modifications are proposed for two algorithms, Monte Carlo Tree Search and Rolling Horizon Evolutionary Algorithms,
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Zhou, Xiao, Song Zhou, Xingang Mou, and Yi He. "Multirobot Collaborative Pursuit Target Robot by Improved MADDPG." Computational Intelligence and Neuroscience 2022 (February 25, 2022): 1–10. http://dx.doi.org/10.1155/2022/4757394.

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Policy formulation is one of the main problems in multirobot systems, especially in multirobot pursuit-evasion scenarios, where both sparse rewards and random environment changes bring great difficulties to find better strategy. Existing multirobot decision-making methods mostly use environmental rewards to promote robots to complete the target task that cannot achieve good results. This paper proposes a multirobot pursuit method based on improved multiagent deep deterministic policy gradient (MADDPG), which solves the problem of sparse rewards in multirobot pursuit-evasion scenarios by combin
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Teses / dissertações sobre o assunto "Sparse Reward"

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Hanski, Jari, and Kaan Baris Biçak. "An Evaluation of the Unity Machine Learning Agents Toolkit in Dense and Sparse Reward Video Game Environments." Thesis, Uppsala universitet, Institutionen för speldesign, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-444982.

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In computer games, one use case for artificial intelligence is used to create interesting problems for the player. To do this new techniques such as reinforcement learning allows game developers to create artificial intelligence agents with human-like or superhuman abilities. The Unity ML-agents toolkit is a plugin that provides game developers with access to reinforcement algorithms without expertise in machine learning. In this paper, we compare reinforcement learning methods and provide empirical training data from two different environments. First, we describe the chosen reinforcement meth
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Castanet, Nicolas. "Automatic state representation and goal selection in unsupervised reinforcement learning." Electronic Thesis or Diss., Sorbonne université, 2025. http://www.theses.fr/2025SORUS005.

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Au cours des dernières années, l'apprentissage par renforcement a connu un succès considérable en entrainant des agents spécialisés capables de dépasser radicalement les performances humaines dans des jeux complexes comme les échecs ou le go, ou dans des applications robotiques. Ces agents manquent souvent de polyvalence, ce qui oblige l'ingénierie humaine à concevoir leur comportement pour des tâches spécifiques avec un signal de récompense prédéfini, limitant ainsi leur capacité à faire face à de nouvelles circonstances. La spécialisation de ces agents se traduit par de faibles capacités de
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Paolo, Giuseppe. "Learning in Sparse Rewards setting through Quality Diversity algorithms." Electronic Thesis or Diss., Sorbonne université, 2021. http://www.theses.fr/2021SORUS400.

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Les agents incarnés, qu'ils soient naturels ou artificiels, peuvent apprendre à interagir avec l'environnement dans lequel ils se trouvent par un processus d'essais et d'erreurs. Ce processus peut être formalisé dans le cadre de l'apprentissage par renforcement, dans lequel l'agent effectue une action dans l'environnement et observe son résultat par le biais d'une observation et d'un signal de récompense. C'est le signal de récompense qui indique à l'agent la qualité de l'action effectuée par rapport à la tâche. Cela signifie que plus une récompense est donnée, plus il est facile d'améliorer l
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Beretta, Davide. "Experience Replay in Sparse Rewards Problems using Deep Reinforcement Techniques." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/17531/.

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In questo lavoro si introduce il lettore al Reinforcement Learning, un'area del Machine Learning su cui negli ultimi anni è stata fatta molta ricerca. In seguito vengono presentate alcune modifiche ad ACER, un algoritmo noto e molto interessante che fa uso di Experience Replay. Lo scopo è quello di cercare di aumentarne le performance su problemi generali ma in particolar modo sugli sparse reward problem. Per verificare la bontà delle idee proposte è utilizzato Montezuma's Revenge, un gioco sviluppato per Atari 2600 e considerato tra i più difficili da trattare.
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Parisi, Simone [Verfasser], Jan [Akademischer Betreuer] Peters, and Joschka [Akademischer Betreuer] Boedeker. "Reinforcement Learning with Sparse and Multiple Rewards / Simone Parisi ; Jan Peters, Joschka Boedeker." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://d-nb.info/1203301545/34.

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Benini, Francesco. "Predicting death in games with deep reinforcement learning." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2020. http://amslaurea.unibo.it/20755/.

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Il contesto in cui si pone l'elaborato è una branca del machine learning, chiamato reinforcement learning. Quest'elaborato si pone come obiettivo di migliorare il lavoro sviluppato dal collega M. Conciatori. In questa tesi ci si vuole soffermare sui giochi con ricompense molto sparse, dove la soluzione precedente non era riuscita a conseguire traguardi. I giochi con ricompense sparse sono quelli in cui l'agente prima di ottenere un premio, che gli faccia comprendere che sta eseguendo la sequenza di azioni corretta, deve compiere un gran numero di azioni. Tra i giochi con queste caratterist
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Gallouedec, Quentin. "Toward the generalization of reinforcement learning." Electronic Thesis or Diss., Ecully, Ecole centrale de Lyon, 2024. http://www.theses.fr/2024ECDL0013.

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L’apprentissage par renforcement conventionnel implique l’entraînement d’un agent unimodal sur une tâche unique et bien définie, guidé par un signal de récompense optimisé pour le gradient. Ce cadre ne nous permet pas d’envisager un agent d’apprentissage adapté aux problèmes du monde réel impliquant des flux de diverses modalités, des tâches multiples, souvent mal définies, voire pas définies du tout. C’est pourquoi nous préconisons une transition vers un cadre plus général, visant à créer des algorithmes d’apprentissage par renforcement plus adaptables et intrinsèquement polyvalents. Pour pro
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Junyent, Barbany Miquel. "Width-Based Planning and Learning." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/672779.

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Optimal sequential decision making is a fundamental problem to many diverse fields. In recent years, Reinforcement Learning (RL) methods have experienced unprecedented success, largely enabled by the use of deep learning models, reaching human-level performance in several domains, such as the Atari video games or the ancient game of Go. In contrast to the RL approach in which the agent learns a policy from environment interaction samples, ignoring the structure of the problem, the planning approach for decision making assumes known models for the agent's goals and domain dynamics, and fo
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Parisi, Simone. "Reinforcement Learning with Sparse and Multiple Rewards." Phd thesis, 2020. https://tuprints.ulb.tu-darmstadt.de/11372/1/THESIS.PDF.

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Over the course of the last decade, the framework of reinforcement learning has developed into a promising tool for learning a large variety of task. The idea of reinforcement learning is, at its core, very simple yet effective. The learning agent is left to explore the world by performing actions based on its observations of the state of the world, and in turn receives a feedback, called reward, assessing the quality of its behavior. However, learning soon becomes challenging and even impractical as the complexity of the environment and of the task increase. In particular, learning without
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Chi, Lu-cheng, and 紀律呈. "An Improved Deep Reinforcement Learning with Sparse Rewards." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/eq94pr.

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碩士<br>國立中山大學<br>電機工程學系研究所<br>107<br>In reinforcement learning, how an agent explores in an environment with sparse rewards is a long-standing problem. An improved deep reinforcement learning described in this thesis encourages an agent to explore unvisited environmental states in an environment with sparse rewards. In deep reinforcement learning, an agent directly uses an image observation from environment as an input to the neural network. However, some neglected observations from environment, such as depth, might provide valuable information. An improved deep reinforcement learning described
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Livros sobre o assunto "Sparse Reward"

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Rudyard, Kipling. Rewards and fairies. Doubleday, Page, 1989.

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Rudyard, Kipling. Puck of Pook's Hill ; and, Rewards and fairies. Oxford University Press, 1992.

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3

Persson, Fabian. Women at the Early Modern Swedish Court. Amsterdam University Press, 2021. http://dx.doi.org/10.5117/9789463725200.

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What was possible for a woman to achieve at an early modern court? By analysing the experiences of a wide range of women at the court of Sweden, this book demonstrates the opportunities open to women who served at, and interacted with, the court; the complexities of women's agency in a court society; and, ultimately, the precariousness of power. In doing so, it provides an institutional context to women's lives at court, charting the full extent of the rewards that they might obtain, alongside the social and institutional constrictions that they faced. Its longue durée approach, moreover, clar
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Prima. Official Sega Genesis: Power Tips Book. Prima Publishing, 1992.

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Mcdermott, Leeanne. GamePro Presents: Sega Genesis Games Secrets: Greatest Tips. Prima Publishing, 1992.

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Sandler, Corey. Official Sega Genesis and Game Gear strategies, 3RD Edition. Bantam Books, 1992.

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Rudyard, Kipling. Rewards & Fairies. Amereon Ltd, 1988.

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Rudyard, Kipling. Rewards and Fairies. Createspace Independent Publishing Platform, 2016.

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Rudyard, Kipling. Rewards and Fairies. Independently Published, 2021.

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Rudyard, Kipling. Rewards and Fairies. Pan MacMillan, 2016.

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Capítulos de livros sobre o assunto "Sparse Reward"

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Hensel, Maximilian. "Exploration Methods in Sparse Reward Environments." In Reinforcement Learning Algorithms: Analysis and Applications. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-41188-6_4.

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Moy, Glennn, and Slava Shekh. "Evolution Strategies for Sparse Reward Gridworld Environments." In AI 2022: Advances in Artificial Intelligence. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-22695-3_19.

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Jeewa, Asad, Anban W. Pillay, and Edgar Jembere. "Learning to Generalise in Sparse Reward Navigation Environments." In Artificial Intelligence Research. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-66151-9_6.

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Chen, Zhongpeng, and Qiang Guan. "Continuous Exploration via Multiple Perspectives in Sparse Reward Environment." In Pattern Recognition and Computer Vision. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-8435-0_5.

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Lei, Hejun, Paul Weng, Juan Rojas, and Yisheng Guan. "Planning with Q-Values in Sparse Reward Reinforcement Learning." In Intelligent Robotics and Applications. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13844-7_56.

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Fu, Yupeng, Yuan Xiao, Jun Fang, Xiangyang Deng, Ziqiang Zhu, and Limin Zhang. "Distributed Advantage-Based Weights Reshaping Algorithm with Sparse Reward." In Lecture Notes in Computer Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-7181-3_31.

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Le, Bang-Giang, Thi-Linh Hoang, Hai-Dang Kieu, and Viet-Cuong Ta. "Structural and Compact Latent Representation Learning on Sparse Reward Environments." In Intelligent Information and Database Systems. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-5837-5_4.

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Wu, Feng, and Xiaoping Chen. "Solving Large-Scale and Sparse-Reward DEC-POMDPs with Correlation-MDPs." In RoboCup 2007: Robot Soccer World Cup XI. Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-68847-1_18.

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Mizukami, Naoki, Jun Suzuki, Hirotaka Kameko, and Yoshimasa Tsuruoka. "Exploration Bonuses Based on Upper Confidence Bounds for Sparse Reward Games." In Lecture Notes in Computer Science. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71649-7_14.

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Kang, Yongxin, Enmin Zhao, Yifan Zang, Kai Li, and Junliang Xing. "Towards a Unified Benchmark for Reinforcement Learning in Sparse Reward Environments." In Communications in Computer and Information Science. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-1639-9_16.

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Trabalhos de conferências sobre o assunto "Sparse Reward"

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Hossain, Jumman, Abu-Zaher Faridee, Nirmalya Roy, Jade Freeman, Timothy Gregory, and Theron Trout. "TopoNav: Topological Navigation for Efficient Exploration in Sparse Reward Environments." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802380.

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Huang, Chao, Yibei Guo, Zhihui Zhu, Mei Si, Daniel Blankenberg, and Rui Liu. "Quantum Exploration-based Reinforcement Learning for Efficient Robot Path Planning in Sparse-Reward Environment." In 2024 33rd IEEE International Conference on Robot and Human Interactive Communication (ROMAN). IEEE, 2024. http://dx.doi.org/10.1109/ro-man60168.2024.10731199.

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Yang, Kai, Zhirui Fang, Xiu Li, and Jian Tao. "CMBE: Curiosity-driven Model-Based Exploration for Multi-Agent Reinforcement Learning in Sparse Reward Settings." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650769.

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Farkaš, Igor. "Explaining Internal Representations in Deep Networks: Adversarial Vulnerability of Image Classifiers and Learning Sequential Tasks with Sparse Reward." In 2025 IEEE 23rd World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2025. https://doi.org/10.1109/sami63904.2025.10883317.

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Xi, Lele, Hongkun Wang, Zhijie Li, and Changchun Hua. "An Experience Replay Approach Based on SSIM to Solve the Sparse Reward Problem in Pursuit Evasion Game*." In 2024 China Automation Congress (CAC). IEEE, 2024. https://doi.org/10.1109/cac63892.2024.10864615.

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Wang, Guojian, Faguo Wu, and Xiao Zhang. "Trajectory-Oriented Policy Optimization with Sparse Rewards." In 2024 2nd International Conference on Intelligent Perception and Computer Vision (CIPCV). IEEE, 2024. http://dx.doi.org/10.1109/cipcv61763.2024.00023.

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Cheng, Hao, Jiahang Cao, Erjia Xiao, Mengshu Sun, and Renjing Xu. "Gaining the Sparse Rewards by Exploring Lottery Tickets in Spiking Neural Networks." In 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2024. https://doi.org/10.1109/iros58592.2024.10802854.

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Huang, Yuming, Bin Ren, Ziming Xu, and Lianghong Wu. "MRHER: Model-based Relay Hindsight Experience Replay for Sequential Object Manipulation Tasks with Sparse Rewards." In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024. http://dx.doi.org/10.1109/ijcnn60899.2024.10650959.

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Madnani, Mayur. "Enhancing AWS DeepRacer Performance: A Study on Reward Functions, Action Spaces, and Hyperparameter Tuning." In 2024 17th International Conference on Development in eSystem Engineering (DeSE). IEEE, 2024. https://doi.org/10.1109/dese63988.2024.10911895.

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Tian, Yuhe, Ayooluwa Akintola, Yazhou Jiang, et al. "Reinforcement Learning-Driven Process Design: A Hydrodealkylation Example." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.119603.

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In this work, we present a follow-up work of reinforcement learning (RL)-driven process design using the Institute for Design of Advanced Energy Systems Process Systems Engineering (IDAES-PSE) Framework. Herein, process designs are generated as stream inlet-outlet matrices and optimized using the IDAES platform, the objective function value of which is the reward to RL agent. Deep Q-Network is employed as the RL agent including a series of convolutional neural network layers and fully connected layers to compute the actions of adding or removing any stream connections, thus creating a new proc
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Relatórios de organizações sobre o assunto "Sparse Reward"

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Erik Lyngdorf, Niels, Selina Thelin Ruggaard, Kathrin Otrel-Cass, and Eamon Costello. The Hacking Innovative Pedagogies (HIP) framework: - Rewilding the digital learning ecology. Aalborg University, 2023. http://dx.doi.org/10.54337/aau602808725.

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The HIP framework aims to guide higher education (HE) teachers and researchers to reconsider and reflect on how to rethink HE pedagogy in new and different ways. It builds on insights from the report Hacking Innovative Pedagogy: Innovation and Digitisation to Rewild Higher Education. A Commented Atlas (Beskorsa, et al., 2023) and incorporates the spirit of rewilding and hacking pedagogies to inspire new professional communities focused on innovating digital education. The framework considers and guides the development of teachers’ digital pedagogy competences through an inclusive bottom-up app
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Murray, Chris, Keith Williams, Norrie Millar, Monty Nero, Amy O'Brien, and Damon Herd. A New Palingenesis. University of Dundee, 2022. http://dx.doi.org/10.20933/100001273.

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Robert Duncan Milne (1844-99), from Cupar, Fife, was a pioneering author of science fiction stories, most of which appeared in San Francisco’s Argonaut magazine in the 1880s and ’90s. SF historian Sam Moskowitz credits Milne with being the first full-time SF writer, and his contribution to the genre is arguably greater than anyone else including Stevenson and Conan Doyle, yet it has all but disappeared into oblivion. Milne was fascinated by science. He drew on the work of Scottish physicists and inventors such as James Clark Maxwell and Alexander Graham Bell into the possibilities of electroma
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