Log in

Relevant bibliographies by topics / Goal-conditioned reinforcement learning / Journal articles

To see the other types of publications on this topic, follow the link: Goal-conditioned reinforcement learning.

Journal articles on the topic 'Goal-conditioned reinforcement learning'

Author: Grafiati

Published: 25 May 2024

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Goal-conditioned reinforcement learning.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Yin, Xiangyu, Sihao Wu, Jiaxu Liu, et al. "Representation-Based Robustness in Goal-Conditioned Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21761–69. http://dx.doi.org/10.1609/aaai.v38i19.30176.

Full text

Abstract:

While Goal-Conditioned Reinforcement Learning (GCRL) has gained attention, its algorithmic robustness against adversarial perturbations remains unexplored. The attacks and robust representation training methods that are designed for traditional RL become less effective when applied to GCRL. To address this challenge, we first propose the Semi-Contrastive Representation attack, a novel approach inspired by the adversarial contrastive attack. Unlike existing attacks in RL, it only necessitates information from the policy function and can be seamlessly implemented during deployment. Then, to miti

APA, Harvard, Vancouver, ISO, and other styles

2

Levine, Alexander, and Soheil Feizi. "Goal-Conditioned Q-learning as Knowledge Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8500–8509. http://dx.doi.org/10.1609/aaai.v37i7.26024.

Full text

Abstract:

Many applications of reinforcement learning can be formalized as goal-conditioned environments, where, in each episode, there is a "goal" that affects the rewards obtained during that episode but does not affect the dynamics. Various techniques have been proposed to improve performance in goal-conditioned environments, such as automatic curriculum generation and goal relabeling. In this work, we explore a connection between off-policy reinforcement learning in goal-conditioned settings and knowledge distillation. In particular: the current Q-value function and the target Q-value estimate are b

APA, Harvard, Vancouver, ISO, and other styles

3

YAMADA, Takaya, and Koich OGAWARA. "Goal-Conditioned Reinforcement Learning with Latent Representations using Contrastive Learning." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2021 (2021): 1P1—I15. http://dx.doi.org/10.1299/jsmermd.2021.1p1-i15.

Full text

APA, Harvard, Vancouver, ISO, and other styles

4

Qian, Zhifeng, Mingyu You, Hongjun Zhou, and Bin He. "Weakly Supervised Disentangled Representation for Goal-Conditioned Reinforcement Learning." IEEE Robotics and Automation Letters 7, no. 2 (2022): 2202–9. http://dx.doi.org/10.1109/lra.2022.3141148.

Full text

APA, Harvard, Vancouver, ISO, and other styles

5

TANIGUCHI, Asuto, Fumihiro SASAKI, and Ryota YAMASHINA. "Goal-Conditioned Reinforcement Learning with Extended Floyd-Warshall method." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2020 (2020): 2A1—L01. http://dx.doi.org/10.1299/jsmermd.2020.2a1-l01.

Full text

APA, Harvard, Vancouver, ISO, and other styles

6

Li, Yao, YuHui Wang, and XiaoYang Tan. "Highly valued subgoal generation for efficient goal-conditioned reinforcement learning." Neural Networks 181 (January 2025): 106825. http://dx.doi.org/10.1016/j.neunet.2024.106825.

Full text

APA, Harvard, Vancouver, ISO, and other styles

7

Elguea-Aguinaco, Íñigo, Antonio Serrano-Muñoz, Dimitrios Chrysostomou, Ibai Inziarte-Hidalgo, Simon Bøgh, and Nestor Arana-Arexolaleiba. "Goal-Conditioned Reinforcement Learning within a Human-Robot Disassembly Environment." Applied Sciences 12, no. 22 (2022): 11610. http://dx.doi.org/10.3390/app122211610.

Full text

Abstract:

The introduction of collaborative robots in industrial environments reinforces the need to provide these robots with better cognition to accomplish their tasks while fostering worker safety without entering into safety shutdowns that reduce workflow and production times. This paper presents a novel strategy that combines the execution of contact-rich tasks, namely disassembly, with real-time collision avoidance through machine learning for safe human-robot interaction. Specifically, a goal-conditioned reinforcement learning approach is proposed, in which the removal direction of a peg, of vary

APA, Harvard, Vancouver, ISO, and other styles

8

Liu, Bo, Yihao Feng, Qiang Liu, and Peter Stone. "Metric Residual Network for Sample Efficient Goal-Conditioned Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 7 (2023): 8799–806. http://dx.doi.org/10.1609/aaai.v37i7.26058.

Full text

Abstract:

Goal-conditioned reinforcement learning (GCRL) has a wide range of potential real-world applications, including manipulation and navigation problems in robotics. Especially in such robotics tasks, sample efficiency is of the utmost importance for GCRL since, by default, the agent is only rewarded when it reaches its goal. While several methods have been proposed to improve the sample efficiency of GCRL, one relatively under-studied approach is the design of neural architectures to support sample efficiency. In this work, we introduce a novel neural architecture for GCRL that achieves significa

APA, Harvard, Vancouver, ISO, and other styles

9

Ding, Hongyu, Yuanze Tang, Qing Wu, Bo Wang, Chunlin Chen, and Zhi Wang. "Magnetic Field-Based Reward Shaping for Goal-Conditioned Reinforcement Learning." IEEE/CAA Journal of Automatica Sinica 10, no. 12 (2023): 2233–47. http://dx.doi.org/10.1109/jas.2023.123477.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Faccio, Francesco, Vincent Herrmann, Aditya Ramesh, Louis Kirsch, and Jürgen Schmidhuber. "Goal-Conditioned Generators of Deep Policies." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 6 (2023): 7503–11. http://dx.doi.org/10.1609/aaai.v37i6.25912.

Full text

Abstract:

Goal-conditioned Reinforcement Learning (RL) aims at learning optimal policies, given goals encoded in special command inputs. Here we study goal-conditioned neural nets (NNs) that learn to generate deep NN policies in form of context-specific weight matrices, similar to Fast Weight Programmers and other methods from the 1990s. Using context commands of the form ``generate a policy that achieves a desired expected return,'' our NN generators combine powerful exploration of parameter space with generalization across commands to iteratively find better and better policies. A form of weight-shari

APA, Harvard, Vancouver, ISO, and other styles

11

Xu, Jiawei, Shuxing Li, Rui Yang, Chun Yuan, and Lei Han. "Efficient Multi-Goal Reinforcement Learning via Value Consistency Prioritization." Journal of Artificial Intelligence Research 77 (June 5, 2023): 355–76. http://dx.doi.org/10.1613/jair.1.14398.

Full text

Abstract:

Goal-conditioned reinforcement learning (RL) with sparse rewards remains a challenging problem in deep RL. Hindsight Experience Replay (HER) has been demonstrated to be an effective solution, where HER replaces desired goals in failed experiences with practically achieved states. Existing approaches mainly focus on either exploration or exploitation to improve the performance of HER. From a joint perspective, exploiting specific past experiences can also implicitly drive exploration. Therefore, we concentrate on prioritizing both original and relabeled samples for efficient goal-conditioned RL

APA, Harvard, Vancouver, ISO, and other styles

12

Liu, Jinxin, Donglin Wang, Qiangxing Tian, and Zhengyu Chen. "Learn Goal-Conditioned Policy with Intrinsic Motivation for Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 7 (2022): 7558–66. http://dx.doi.org/10.1609/aaai.v36i7.20721.

Full text

Abstract:

It is of significance for an agent to autonomously explore the environment and learn a widely applicable and general-purpose goal-conditioned policy that can achieve diverse goals including images and text descriptions. Considering such perceptually-specific goals, one natural approach is to reward the agent with a prior non-parametric distance over the embedding spaces of states and goals. However, this may be infeasible in some situations, either because it is unclear how to choose suitable measurement, or because embedding (heterogeneous) goals and states is non-trivial. The key insight of

APA, Harvard, Vancouver, ISO, and other styles

13

Lei, Xing, Xuetao Zhang, and Donglin Wang. "MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 17 (2025): 18172–80. https://doi.org/10.1609/aaai.v39i17.33999.

Full text

Abstract:

Recently, a state-of-the-art series of algorithms—Goal-Conditioned Weighted Supervised Learning (GCWSL) methods—has been introduced to address the challenges inherent in offline goal-conditioned reinforcement learning (RL). GCWSL optimizes a lower bound on the goal-conditioned RL objective and has demonstrated exceptional performance across a range of goal-reaching tasks, offering a simple, effective, and stable solution. Nonetheless, researches has revealed a critical limitation in GCWSL: the absence of trajectory stitching capabilities. In response, goal data augmentation strategies have bee

APA, Harvard, Vancouver, ISO, and other styles

14

Jang, Seongwon, Hyemi Jeong, and Hyunseok Yang. "MURM: Utilization of Multi-Views for Goal-Conditioned Reinforcement Learning in Robotic Manipulation." Robotics 12, no. 4 (2023): 119. http://dx.doi.org/10.3390/robotics12040119.

Full text

Abstract:

We present a novel framework, multi-view unified reinforcement learning for robotic manipulation (MURM), which efficiently utilizes multiple camera views to train a goal-conditioned policy for a robot to perform complex tasks. The MURM framework consists of three main phases: (i) demo collection from an expert, (ii) representation learning, and (iii) offline reinforcement learning. In the demo collection phase, we design a scripted expert policy that uses privileged information, such as Cartesian coordinates of a target and goal, to solve the tasks. We add noise to the expert policy to provide

APA, Harvard, Vancouver, ISO, and other styles

15

Colas, Cédric, Tristan Karch, Olivier Sigaud, and Pierre-Yves Oudeyer. "Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: A Short Survey." Journal of Artificial Intelligence Research 74 (July 9, 2022): 1159–99. http://dx.doi.org/10.1613/jair.1.13554.

Full text

Abstract:

Building autonomous machines that can explore open-ended environments, discover possible interactions and build repertoires of skills is a general objective of artificial intelligence. Developmental approaches argue that this can only be achieved by autotelic agents: intrinsically motivated learning agents that can learn to represent, generate, select and solve their own problems. In recent years, the convergence of developmental approaches with deep reinforcement learning (RL) methods has been leading to the emergence of a new field: developmental reinforcement learning. Developmental RL is c

APA, Harvard, Vancouver, ISO, and other styles

16

Zhong, Haojun, Zhenlei Wang, and Yuzhe Hao. "A goal-conditioned offline reinforcement learning algorithm and its application to quad-rotors." Engineering Applications of Artificial Intelligence 152 (July 2025): 110678. https://doi.org/10.1016/j.engappai.2025.110678.

Full text

APA, Harvard, Vancouver, ISO, and other styles

17

Ma, Haozhe, Thanh Vinh Vo, and Tze-Yun Leong. "Human-AI Collaborative Sub-Goal Optimization in Hierarchical Reinforcement Learning." Proceedings of the AAAI Symposium Series 1, no. 1 (2023): 86–89. http://dx.doi.org/10.1609/aaaiss.v1i1.27481.

Full text

Abstract:

Hierarchical reinforcement learning often involves human expertise in defining multiple sub-goals to decompose complex objectives into relevant sub-tasks. However, manually specifying these sub-goals is labor-intensive, costly, and prone to introducing biases or misleading the agent. To overcome these challenges, we propose a collaborative human-AI algorithm that seamlessly integrates with hierarchical models to automatically update prior knowledge and optimize candidate sub-goals. Our algorithm can be easily incorporated into a wide range of goal-conditioned frameworks. We evaluate our approa

APA, Harvard, Vancouver, ISO, and other styles

18

Zhou, Li, and Kevin Small. "Inverse Reinforcement Learning with Natural Language Goals." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 12 (2021): 11116–24. http://dx.doi.org/10.1609/aaai.v35i12.17326.

Full text

Abstract:

Humans generally use natural language to communicate task requirements to each other. Ideally, natural language should also be usable for communicating goals to autonomous machines (e.g., robots) to minimize friction in task specification. However, understanding and mapping natural language goals to sequences of states and actions is challenging. Specifically, existing work along these lines has encountered difficulty in generalizing learned policies to new natural language goals and environments. In this paper, we propose a novel adversarial inverse reinforcement learning algorithm to learn a

APA, Harvard, Vancouver, ISO, and other styles

19

Lu, Yuxiao, Arunesh Sinha, and Pradeep Varakantham. "Handling Long and Richly Constrained Tasks through Constrained Hierarchical Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 19 (2024): 21368–77. http://dx.doi.org/10.1609/aaai.v38i19.30132.

Full text

Abstract:

Safety in goal directed Reinforcement Learning (RL) settings has typically been handled through constraints over trajectories and have demonstrated good performance in primarily short horizon tasks. In this paper, we are specifically interested in the problem of solving temporally extended decision making problems such as robots cleaning different areas in a house while avoiding slippery and unsafe areas (e.g., stairs) and retaining enough charge to move to a charging dock; in the presence of complex safety constraints. Our key contribution is a (safety) Constrained Search with Hierarchical Re

APA, Harvard, Vancouver, ISO, and other styles

20

Kim, Sungyoon, Yunseon Choi, Daiki E. Matsunaga, and Kee-Eung Kim. "Stitching Sub-trajectories with Conditional Diffusion Model for Goal-Conditioned Offline RL." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 12 (2024): 13160–67. http://dx.doi.org/10.1609/aaai.v38i12.29215.

Full text

Abstract:

Offline Goal-Conditioned Reinforcement Learning (Offline GCRL) is an important problem in RL that focuses on acquiring diverse goal-oriented skills solely from pre-collected behavior datasets. In this setting, the reward feedback is typically absent except when the goal is achieved, which makes it difficult to learn policies especially from a finite dataset of suboptimal behaviors. In addition, realistic scenarios involve long-horizon planning, which necessitates the extraction of useful skills within sub-trajectories. Recently, the conditional diffusion model has been shown to be a promising

APA, Harvard, Vancouver, ISO, and other styles

21

Kim, Hyeonmin, Jongkwan Choi, Hyungrok Do, and Gyeong Taek Lee. "A Fully Controllable UAV Using Curriculum Learning and Goal-Conditioned Reinforcement Learning: From Straight Forward to Round Trip Missions." Drones 9, no. 1 (2024): 26. https://doi.org/10.3390/drones9010026.

Full text

Abstract:

The focus of unmanned aerial vehicle (UAV) path planning includes challenging tasks such as obstacle avoidance and efficient target reaching in complex environments. Building upon these fundamental challenges, an additional need exists for agents that can handle diverse missions like round-trip navigation without requiring retraining for each specific task. In our study, we present a path planning method using reinforcement learning (RL) for a fully controllable UAV agent. We combine goal-conditioned RL and curriculum learning to enable agents to progressively master increasingly complex missi

APA, Harvard, Vancouver, ISO, and other styles

22

Wang, Guan, Haoyi Niu, Jianxiong Li, Li Jiang, Jianming Hu, and Xianyuan Zhan. "Are Expressive Models Truly Necessary for Offline RL?" Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 20 (2025): 21062–70. https://doi.org/10.1609/aaai.v39i20.35403.

Full text

Abstract:

Among various branches of offline reinforcement learning (RL) methods, goal-conditioned supervised learning (GCSL) has gained increasing popularity as it formulates the offline RL problem as a sequential modeling task, therefore bypassing the notoriously difficult credit assignment challenge of value learning in conventional RL paradigm. Sequential modeling, however, requires capturing accurate dynamics across long horizons in trajectory data to ensure reasonable policy performance. To meet this requirement, leveraging large, expressive models has become a popular choice in recent literature,

APA, Harvard, Vancouver, ISO, and other styles

23

Yang, Xinyi, Xinting Yang, Chao Yu, et al. "MASP: Scalable Graph-Based Planning Towards Multi-UAV Navigation." Drones 9, no. 7 (2025): 463. https://doi.org/10.3390/drones9070463.

Full text

Abstract:

This work investigates multi-UAV navigation tasks where multiple drones need to reach initially unassigned goals in a limited time. Reinforcement learning (RL) has recently become a popular approach for such tasks. However, RL struggles with low sample efficiency when directly exploring (nearly) optimal policies in a large exploration space, especially with an increased number of drones (e.g., 10+ drones) or in complex environments (e.g., a 3D quadrotor simulator). To address these challenges, this paper proposes Multi-UAV Scalable Graph-based Planner (MASP), a goal-conditioned hierarchical pl

APA, Harvard, Vancouver, ISO, and other styles

24

Kim, Yujin, Woo-Jin Ahn, Sun-Ho Jang, Myo-Taeg Lim, and Dong-Sung Pae. "A Reinforcement Learning Approach to Dynamic Trajectory Optimization with Consideration of Imbalanced Sub-Goals in Self-Driving Vehicles." Applied Sciences 14, no. 12 (2024): 5213. http://dx.doi.org/10.3390/app14125213.

Full text

Abstract:

Goal-conditioned Reinforcement Learning (RL) holds promise for addressing intricate control challenges by enabling agents to learn and execute desired skills through separate decision modules. However, the irregular occurrence of required skills poses a significant challenge to effective learning. In this paper, we demonstrate the detrimental effects of this imbalanced skill (sub-goal) distribution and propose a novel training approach, Classified Experience Replay (CER), designed to mitigate this challenge. We demonstrate that adapting our method to conventional RL methods significantly enhan

APA, Harvard, Vancouver, ISO, and other styles

25

Grossberg, Stephen, and John W. L. Merrill. "The Hippocampus and Cerebellum in Adaptively Timed Learning, Recognition, and Movement." Journal of Cognitive Neuroscience 8, no. 3 (1996): 257–77. http://dx.doi.org/10.1162/jocn.1996.8.3.257.

Full text

Abstract:

The concepts of declarative memory and procedural memory have been used to distinguish two basic types of learning. A neural network model suggests how such memory processes work together as recognition learning, reinforcement learning, and sensorimotor learning take place during adaptive behaviors. To coordinate these processes, the hippocampal formation and cerebellum each contains circuits that learn to adaptively time their outputs. Within the model, hippocampal timing helps to maintain attention on motivationally salient goal objects during variable task-related delays, and cerebellar tim

APA, Harvard, Vancouver, ISO, and other styles

26

Anam, Mamoona, Dr Kantilal P. Rane, Ali Alenezi, Ruby Mishra, Dr Swaminathan Ramamurthy, and Ferdin Joe John Joseph. "Content Classification Tasks with Data Preprocessing Manifestations." Webology 19, no. 1 (2022): 1413–30. http://dx.doi.org/10.14704/web/v19i1/web19094.

Full text

Abstract:

Deep reinforcement learning has a major hurdle in terms of data efficiency. We solve this challenge by pretraining an encoder with unlabeled input, which is subsequently finetuned on a tiny quantity of task-specific input. We use a mixture of latent dynamics modelling and unsupervised goal-conditioned RL to encourage learning representations that capture various elements of the underlying MDP. Our approach significantly outperforms previous work combining offline representation pretraining with task-specific finetuning when limited to 100k steps of interaction on Atari games (equivalent to two

APA, Harvard, Vancouver, ISO, and other styles

27

Mutti, Mirco, and Marcello Restelli. "An Intrinsically-Motivated Approach for Learning Highly Exploring and Fast Mixing Policies." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (2020): 5232–39. http://dx.doi.org/10.1609/aaai.v34i04.5968.

Full text

Abstract:

What is a good exploration strategy for an agent that interacts with an environment in the absence of external rewards? Ideally, we would like to get a policy driving towards a uniform state-action visitation (highly exploring) in a minimum number of steps (fast mixing), in order to ease efficient learning of any goal-conditioned policy later on. Unfortunately, it is remarkably arduous to directly learn an optimal policy of this nature. In this paper, we propose a novel surrogate objective for learning highly exploring and fast mixing policies, which focuses on maximizing a lower bound to the

APA, Harvard, Vancouver, ISO, and other styles

28

Zhang, Zuyuan, Hanhan Zhou, Mahdi Imani, Taeyoung Lee, and Tian Lan. "Learning to Collaborate with Unknown Agents in the Absence of Reward." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 13 (2025): 14502–11. https://doi.org/10.1609/aaai.v39i13.33589.

Full text

Abstract:

With the advancements of artificial intelligence (AI), emerging scenarios involving close collaboration between AI and other unknown agents are becoming increasingly common. This requires sometimes training AI agents to collaborate with unknown agents in the absence of a reward function -- which may be unavailable to the AI agents or even undefined by the unknown agents themselves -- thus posing news challenges to existing learning algorithms that often require knowing the shared reward. In this paper, we show that effective teaming with unknown agents can be achieved in the absence of a rewar

APA, Harvard, Vancouver, ISO, and other styles

29

Feng, Xiaoyun. "Consistent Experience Replay in High-Dimensional Continuous Control with Decayed Hindsights." Machines 10, no. 10 (2022): 856. http://dx.doi.org/10.3390/machines10100856.

Full text

Abstract:

The manipulation of complex robotics, which is in general high-dimensional continuous control without an accurate dynamic model, summons studies and applications of reinforcement learning (RL) algorithms. Typically, RL learns with the objective of maximizing the accumulated rewards from interactions with the environment. In reality, external rewards are not trivial, which depend on either expert knowledge or domain priors. Recent advances on hindsight experience replay (HER) instead enable a robot to learn from the automatically generated sparse and binary rewards, indicating whether it reache

APA, Harvard, Vancouver, ISO, and other styles

30

Bandura, Albert. "Self-efficacy mechanism in human agency." Psihologìâ ì suspìlʹstvo 90, no. 2 (2024): 63–94. https://doi.org/10.35774/pis2024.02.063.

Full text

Abstract:

The article announces a theoretically complete and methodically substantiated author’s concept of personal self-efficacy, which justifies this integral psychosocial f u n c t i o n a l as one of the basic cognitive mechanisms of human developmental presence in the world. Essentially, it is said about the three-factorially formed (personality, environment, behavior) conscious ability and self-valued ability of a person to carry out behavior in accordance with a complex task or newly appeared problem situation and eventually successfully cope with life problems. The first two subsections, outlin

APA, Harvard, Vancouver, ISO, and other styles

31

Cichosz, Mariusz. "Individual, family and environment as the subject of research in social pedagogy – development and transformations." Papers of Social Pedagogy 7, no. 2 (2018): 6–18. http://dx.doi.org/10.5604/01.3001.0010.8133.

Full text

Abstract:

The cognitive specificity of social pedagogy is its interest in the issues related to social conditionings of human development and, respectively, the specific social conditionings of the upbringing process. The notion has been developed in various directions since the very beginning of the discipline, yet the most clearly visible area seems to be the functioning of individuals, families and broader environment. Simultaneously, it is possible to observe that the issues have been entangled in certain socio-political conditions, the knowledge of which is substantial for the reconstruction and id

APA, Harvard, Vancouver, ISO, and other styles

32

Li, Yao, YuHui Wang, and XiaoYang Tan. "Self-imitation guided goal-conditioned reinforcement learning." Pattern Recognition, August 2023, 109845. http://dx.doi.org/10.1016/j.patcog.2023.109845.

Full text

APA, Harvard, Vancouver, ISO, and other styles

33

Zou, Qiming, and Einoshin Suzuki. "Compact Goal Representation Learning via Information Bottleneck in Goal-Conditioned Reinforcement Learning." IEEE Transactions on Neural Networks and Learning Systems, 2024, 1–14. http://dx.doi.org/10.1109/tnnls.2023.3344880.

Full text

APA, Harvard, Vancouver, ISO, and other styles

34

Li, Jinning, Chen Tang, Masayoshi Tomizuka, and Wei Zhan. "Hierarchical Planning Through Goal-Conditioned Offline Reinforcement Learning." IEEE Robotics and Automation Letters, 2022, 1–8. http://dx.doi.org/10.1109/lra.2022.3190100.

Full text

APA, Harvard, Vancouver, ISO, and other styles

35

Feng, Xiaoyun, Li Jiang, Xudong Yu, et al. "Curriculum Goal-Conditioned Imitation for Offline Reinforcement Learning." IEEE Transactions on Games, 2022, 1–11. http://dx.doi.org/10.1109/tg.2022.3224088.

Full text

APA, Harvard, Vancouver, ISO, and other styles

36

Wang, Mianchu, Yue Jin, and Giovanni Montana. "Goal-conditioned offline reinforcement learning through state space partitioning." Machine Learning, February 5, 2024. http://dx.doi.org/10.1007/s10994-023-06500-z.

Full text

Abstract:

AbstractOffline reinforcement learning (RL) aims to create policies for sequential decision-making using exclusively offline datasets. This presents a significant challenge, especially when attempting to accomplish multiple distinct goals or outcomes within a given scenario while receiving sparse rewards. Prior methods using advantage weighting for offline goal-conditioned learning improve policies monotonically. However, they still face challenges from distribution shift and multi-modality that arise due to conflicting ways to reach a goal. This issue is especially challenging in long-horizon

APA, Harvard, Vancouver, ISO, and other styles

37

Qian, Zhifeng, Mingyu You, Hongjun Zhou, Xuanhui Xu, and Bin He. "Goal-Conditioned Reinforcement Learning with Disentanglement-based Reachability Planning." IEEE Robotics and Automation Letters, 2023, 1–8. http://dx.doi.org/10.1109/lra.2023.3287362.

Full text

APA, Harvard, Vancouver, ISO, and other styles

38

Luo, Yu, Tianying Ji, Fuchun Sun, et al. "Goal-Conditioned Hierarchical Reinforcement Learning With High-Level Model Approximation." IEEE Transactions on Neural Networks and Learning Systems, 2024, 1–15. http://dx.doi.org/10.1109/tnnls.2024.3354061.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Wu, Lisheng, and Ke Chen. "Goal exploration augmentation via pre-trained skills for sparse-reward long-horizon goal-conditioned reinforcement learning." Machine Learning, February 5, 2024. http://dx.doi.org/10.1007/s10994-023-06503-w.

Full text

Abstract:

AbstractReinforcement learning often struggles to accomplish a sparse-reward long-horizon task in a complex environment. Goal-conditioned reinforcement learning (GCRL) has been employed to tackle this difficult problem via a curriculum of easy-to-reach sub-goals. In GCRL, exploring novel sub-goals is essential for the agent to ultimately find the pathway to the desired goal. How to explore novel sub-goals efficiently is one of the most challenging issues in GCRL. Several goal exploration methods have been proposed to address this issue but still struggle to find the desired goals efficiently.

APA, Harvard, Vancouver, ISO, and other styles

40

Lee, Gyeong Taek, and Kangjin Kim. "A Controllable Agent by Subgoals in Path Planning using Goal-Conditioned Reinforcement Learning." IEEE Access, 2023, 1. http://dx.doi.org/10.1109/access.2023.3264264.

Full text

APA, Harvard, Vancouver, ISO, and other styles

41

Song, Wongeun, and Jungwoo Lee. "Ricci planner: Zero-Shot Transfer for Goal-Conditioned Reinforcement Learning via Geometric Flow." IEEE Access, 2024, 1. http://dx.doi.org/10.1109/access.2024.3361478.

Full text

APA, Harvard, Vancouver, ISO, and other styles

42

Lee, GyeongTaek, KangJin Kim, and Jaeyeon Jang. "Real-time path planning of controllable UAV by subgoals using goal-conditioned reinforcement learning." Applied Soft Computing, July 2023, 110660. http://dx.doi.org/10.1016/j.asoc.2023.110660.

Full text

APA, Harvard, Vancouver, ISO, and other styles

43

Jiang, Zhihong, Jiachen Hu, Yan Zhao, Xiao Huang, and Hui Li. "A goal-conditioned policy search method with multi-timescale value function tuning." Robotic Intelligence and Automation, June 11, 2024. http://dx.doi.org/10.1108/ria-11-2023-0167.

Full text

Abstract:

Purpose Current reinforcement learning (RL) algorithms are facing issues such as low learning efficiency and poor generalization performance, which significantly limit their practical application in real robots. This paper aims to adopt a hybrid model-based and model-free policy search method with multi-timescale value function tuning, aiming to allow robots to learn complex motion planning skills in multi-goal and multi-constraint environments with a few interactions. Design/methodology/approach A goal-conditioned model-based and model-free search method with multi-timescale value function tu

APA, Harvard, Vancouver, ISO, and other styles

44

He, Xiangkun, and Chen Lv. "Robotic Control in Adversarial and Sparse Reward Environments: A Robust Goal-Conditioned Reinforcement Learning Approach." IEEE Transactions on Artificial Intelligence, 2023, 1–10. http://dx.doi.org/10.1109/tai.2023.3237665.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Bougie, Nicolas, and Ryutaro Ichise. "Goal-driven active learning." Autonomous Agents and Multi-Agent Systems 35, no. 2 (2021). http://dx.doi.org/10.1007/s10458-021-09527-5.

Full text

Abstract:

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learni

APA, Harvard, Vancouver, ISO, and other styles

46

Sun, Zihao, Xianfeng Yuan, Qingyang Xu, et al. "Goal-Conditioned Reinforcement Learning With Adaptive Intrinsic Curiosity and Universal Value Network Fitting for Robotic Manipulation." IEEE Transactions on Industrial Informatics, 2024, 1–10. https://doi.org/10.1109/tii.2024.3495775.

Full text

APA, Harvard, Vancouver, ISO, and other styles

47

Yazzourh, Sophia, Nicolas Savy, Philippe Saint‐Pierre, and Michael R. Kosorok. "Medical Knowledge Integration Into Reinforcement Learning Algorithms for Dynamic Treatment Regimes." International Statistical Review, June 5, 2025. https://doi.org/10.1111/insr.12617.

Full text

Abstract:

SummaryThe goal of precision medicine is to provide individualised treatment at each stage of chronic diseases, a concept formalised by dynamic treatment regimes (DTR). These regimes adapt treatment strategies based on decision rules learned from clinical data to enhance therapeutic effectiveness. Reinforcement learning (RL) algorithms allow to determine these decision rules conditioned by individual patient data and their medical history. The integration of medical expertise into these models makes possible to increase confidence in treatment recommendations and facilitate the adoption of thi

APA, Harvard, Vancouver, ISO, and other styles

48

Li, Jingchen, Haobin Shi, and Kao-Shing Hwang. "Using Goal-Conditioned Reinforcement Learning With Deep Imitation to Control Robot Arm in Flexible Flat Cable Assembly Task." IEEE Transactions on Automation Science and Engineering, 2023, 1–12. http://dx.doi.org/10.1109/tase.2023.3323307.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Han, Changlin, Zhiyong Peng, Yadong Liu, Jingsheng Tang, Yang Yu, and Zongtan Zhou. "Learning robotic manipulation skills with multiple semantic goals by conservative curiosity-motivated exploration." Frontiers in Neurorobotics 17 (March 7, 2023). http://dx.doi.org/10.3389/fnbot.2023.1089270.

Full text

Abstract:

Reinforcement learning (RL) empowers the agent to learn robotic manipulation skills autonomously. Compared with traditional single-goal RL, semantic-goal-conditioned RL expands the agent capacity to accomplish multiple semantic manipulation instructions. However, due to sparsely distributed semantic goals and sparse-reward agent-environment interactions, the hard exploration problem arises and impedes the agent training process. In traditional RL, curiosity-motivated exploration shows effectiveness in solving the hard exploration problem. However, in semantic-goal-conditioned RL, the performan

APA, Harvard, Vancouver, ISO, and other styles

50

Tam, K. M. M., R. Maia Avelino, D. Kudenko, T. Van Mele, and P. Block. "Well-conditioned AI-assisted sub-matrix selection for numerically stable constrained form-finding of reticulated shells using geometric deep Q-learning." Meccanica, June 19, 2024. http://dx.doi.org/10.1007/s11012-024-01769-3.

Full text

Abstract:

AbstractThe selection of well-conditioned sub-matrices is a critical concern in problems across multiple disciplines, particularly those demanding robust numerical stability. This research introduces an innovative, AI-assisted approach to sub-matrix selection, aimed at enhancing the form-finding of reticulated shell structures under the xy-constrained Force Density Method (also known as Thrust Network Analysis), using independent edge sets. The goal is to select a well-conditioned sub-matrix within a larger matrix with an inherent graph interpretation where each column represents an edge in th

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!