Papers Unsupervised Reinforcement Learning
“Unsupervised Reinforcement Learning” 태그가 달린 논문 57편 · 필터 해제
Unsupervised Skill Discovery through Skill Regions Differentiation
Unsupervised Reinforcement Learning (RL) aims to discover diverse behaviors that can accelerate the learning of downstream tasks. Previous methods typically focus on entropy-based exploration or empowerment-driven skill …
Density EstimationReinforcement Learning (RL)Unsupervised Reinforcement LearningTask Adaptation from Skills: Information Geometry, Disentanglement, and New Objectives for Unsupervised Reinforcement Learning
Unsupervised reinforcement learning (URL) aims to learn general skills for unseen downstream tasks. Mutual Information Skill Learning (MISL) addresses URL by maximizing the mutual information between states and skills bu…
DisentanglementDiversityUnsupervised Reinforcement LearningAutoQD: Automatic Discovery of Diverse Behaviors with Quality-Diversity Optimization
Quality-Diversity (QD) algorithms have shown remarkable success in discovering diverse, high-performing solutions, but rely heavily on hand-crafted behavioral descriptors that constrain exploration to predefined notions …
continuous-controlContinuous ControlDiversitySequential Decision Making+1Interpretable Learning Dynamics in Unsupervised Reinforcement Learning
We present an interpretability framework for unsupervised reinforcement learning (URL) agents, aimed at understanding how intrinsic motivation shapes attention, behavior, and representation learning. We analyze five agen…
DiagnosticDiversityreinforcement-learningReinforcement Learning+2Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models
Unsupervised reinforcement learning (RL) aims at pre-training agents that can solve a wide range of downstream tasks in complex environments. Despite recent advancements, existing approaches suffer from several limitatio…
Humanoid ControlReinforcement Learning (RL)Unsupervised Reinforcement LearningZero-shot GeneralizationExploratory Diffusion Model for Unsupervised Reinforcement Learning
Unsupervised reinforcement learning (URL) aims to pre-train agents by exploring diverse states or skills in reward-free environments, facilitating efficient adaptation to downstream tasks. As the agent cannot access extr…
Efficient Explorationmodelreinforcement-learningReinforcement Learning+2SkiLD: Unsupervised Skill Discovery Guided by Factor Interactions
Unsupervised skill discovery carries the promise that an intelligent agent can learn reusable skills through autonomous, reward-free environment interaction. Existing unsupervised skill discovery methods learn skills by …
DiversityInductive BiasUnsupervised Reinforcement LearningSurprise-Adaptive Intrinsic Motivation for Unsupervised Reinforcement Learning
Both entropy-minimizing and entropy-maximizing (curiosity) objectives for unsupervised reinforcement learning (RL) have been shown to be effective in different environments, depending on the environment's level of natura…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Unsupervised Reinforcement LearningConstrained Ensemble Exploration for Unsupervised Skill Discovery
Unsupervised Reinforcement Learning (RL) provides a promising paradigm for learning useful behaviors via reward-free per-training. Existing methods for unsupervised RL mainly conduct empowerment-driven skill discovery or…
Reinforcement Learning (RL)Unsupervised Reinforcement LearningM2CURL: Sample-Efficient Multimodal Reinforcement Learning via Self-Supervised Representation Learning for Robotic Manipulation
One of the most critical aspects of multimodal Reinforcement Learning (RL) is the effective integration of different observation modalities. Having robust and accurate representations derived from these modalities is key…
reinforcement-learningReinforcement LearningReinforcement Learning (RL)Representation Learning+2Curiosity & Entropy Driven Unsupervised RL in Multiple Environments
The authors of 'Unsupervised Reinforcement Learning in Multiple environments' propose a method, alpha-MEPOL, to tackle unsupervised RL across multiple environments. They pre-train a task-agnostic exploration policy using…
Unsupervised Reinforcement LearningLiFT: Unsupervised Reinforcement Learning with Foundation Models as Teachers
We propose a framework that leverages foundation models as teachers, guiding a reinforcement learning agent to acquire semantically meaningful behavior without human feedback. In our framework, the agent receives task in…
Language ModelingLanguage Modellingreinforcement-learningReinforcement Learning+1Augmenting Unsupervised Reinforcement Learning with Self-Reference
Humans possess the ability to draw on past experiences explicitly when learning new tasks and applying them accordingly. We believe this capacity for self-referencing is especially advantageous for reinforcement learning…
Attributereinforcement-learningReinforcement LearningUnsupervised Reinforcement LearningMETRA: Scalable Unsupervised RL with Metric-Aware Abstraction
Unsupervised pre-training strategies have proven to be highly effective in natural language processing and computer vision. Likewise, unsupervised reinforcement learning (RL) holds the promise of discovering a variety of…
Reinforcement Learning (RL)Unsupervised Pre-trainingUnsupervised Reinforcement LearningExploration with Principles for Diverse AI Supervision
Training large transformers using next-token prediction has given rise to groundbreaking advancements in AI. While this generative AI approach has produced impressive results, it heavily leans on human supervision. Even …
Reinforcement Learning (RL)Unsupervised Reinforcement LearningComSD: Balancing Behavioral Quality and Diversity in Unsupervised Skill Discovery
This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Unsupervised skill discovery seeks to acquire differen…
Contrastive LearningDiversityReinforcement Learning (RL)Unsupervised Reinforcement LearningUnsupervised Discovery of Continuous Skills on a Sphere
Recently, methods for learning diverse skills to generate various behaviors without external rewards have been actively studied as a form of unsupervised reinforcement learning. However, most of the existing methods lear…
MuJoCoUnsupervised Reinforcement LearningA Framework for Provably Stable and Consistent Training of Deep Feedforward Networks
We present a novel algorithm for training deep neural networks in supervised (classification and regression) and unsupervised (reinforcement learning) scenarios. This algorithm combines the standard stochastic gradient d…
Q-Learningreinforcement-learningUnsupervised Reinforcement LearningCRC-RL: A Novel Visual Feature Representation Architecture for Unsupervised Reinforcement Learning
This paper addresses the problem of visual feature representation learning with an aim to improve the performance of end-to-end reinforcement learning (RL) models. Specifically, a novel architecture is proposed that uses…
Decoderreinforcement-learningReinforcement Learning (RL)Representation Learning+1Choreographer: Learning and Adapting Skills in Imagination
Unsupervised skill learning aims to learn a rich repertoire of behaviors without external supervision, providing artificial agents with the ability to control and influence the environment. However, without appropriate k…
Unsupervised Reinforcement Learning