Papers Transfer Reinforcement Learning
“Transfer Reinforcement Learning” 태그가 달린 논문 41편 · 필터 해제
Pessimism Principle Can Be Effective: Towards a Framework for Zero-Shot Transfer Reinforcement Learning
Transfer reinforcement learning aims to derive a near-optimal policy for a target environment with limited data by leveraging abundant data from related source domains. However, it faces two key challenges: the lack of p…
reinforcement-learningReinforcement LearningTransfer LearningTransfer Reinforcement LearningBeam Selection in ISAC using Contextual Bandit with Multi-modal Transformer and Transfer Learning
Sixth generation (6G) wireless technology is anticipated to introduce Integrated Sensing and Communication (ISAC) as a transformative paradigm. ISAC unifies wireless communication and RADAR or other forms of sensing to o…
Beam PredictionDeep Reinforcement LearningIntegrated sensing and communicationISAC+2Hybrid Transfer Reinforcement Learning: Provable Sample Efficiency from Shifted-Dynamics Data
Online Reinforcement learning (RL) typically requires high-stakes online interaction data to learn a policy for a target task. This prompts interest in leveraging historical data to improve sample efficiency. The histori…
Reinforcement Learning (RL)Transfer Reinforcement LearningThe Limits of Transfer Reinforcement Learning with Latent Low-rank Structure
Many reinforcement learning (RL) algorithms are too costly to use in practice due to the large sizes $S, A$ of the problem's state and action space. To resolve this issue, we study transfer RL with latent low rank struct…
Reinforcement Learning (RL)Transfer Reinforcement LearningTransfer Reinforcement Learning in Heterogeneous Action Spaces using Subgoal Mapping
In this paper, we consider a transfer reinforcement learning problem involving agents with different action spaces. Specifically, for any new unseen task, the goal is to use a successful demonstration of this task by an …
reinforcement-learningReinforcement LearningTransfer LearningTransfer Reinforcement LearningSF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning
This paper studies the transfer reinforcement learning (RL) problem where multiple RL problems have different reward functions but share the same underlying transition dynamics. In this setting, the Q-function of each RL…
Deep Reinforcement LearningQ-LearningReinforcement Learning (RL)Transfer Learning+1An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models
Reconfigurable intelligent surface (RIS) becomes a promising technique for 6G networks by reshaping signal propagation in smart radio environments. However, it also leads to significant complexity for network management …
Hierarchical Reinforcement LearningManagementMulti-agent Reinforcement LearningQ-Learning+4Enabling Multi-Agent Transfer Reinforcement Learning via Scenario Independent Representation
Multi-Agent Reinforcement Learning (MARL) algorithms are widely adopted in tackling complex tasks that require collaboration and competition among agents in dynamic Multi-Agent Systems (MAS). However, learning such tasks…
Multi-agent Reinforcement Learningreinforcement-learningSMACSMAC++3Value Explicit Pretraining for Learning Transferable Representations
We propose Value Explicit Pretraining (VEP), a method that learns generalizable representations for transfer reinforcement learning. VEP enables learning of new tasks that share similar objectives as previously learned t…
Transfer LearningTransfer Reinforcement LearningVisual NavigationEfficient Multi-Task and Transfer Reinforcement Learning with Parameter-Compositional Framework
In this work, we investigate the potential of improving multi-task training and also leveraging it for transferring in the reinforcement learning setting. We identify several challenges towards this goal and propose a tr…
reinforcement-learningReinforcement LearningTransfer Reinforcement LearningReinforcement Learning in the Wild with Maximum Likelihood-based Model Transfer
In this paper, we study the problem of transferring the available Markov Decision Process (MDP) models to learn and plan efficiently in an unknown but similar MDP. We refer to it as \textit{Model Transfer Reinforcement L…
reinforcement-learningReinforcement Learning (RL)Transfer Reinforcement LearningProvably Sample-Efficient RL with Side Information about Latent Dynamics
We study reinforcement learning (RL) in settings where observations are high-dimensional, but where an RL agent has access to abstract knowledge about the structure of the state space, as is the case, for example, when a…
reinforcement-learningReinforcement Learning (RL)Transfer Reinforcement LearningTransfer Reinforcement Learning for Differing Action Spaces via Q-Network Representations
Transfer learning approaches in reinforcement learning aim to assist agents in learning their target domains by leveraging the knowledge learned from other agents that have been trained on similar source domains. For exa…
Acrobotreinforcement-learningReinforcement Learning (RL)Transfer Learning+1Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing
Network slicing is a critical technique for 5G communications that covers radio access network (RAN), edge, transport and core slicing.The evolving network architecture requires the orchestration of multiple network reso…
FairnessManagementQ-Learningreinforcement-learning+4AdaRL: What, Where, and How to Adapt in Transfer Reinforcement Learning
One practical challenge in reinforcement learning (RL) is how to make quick adaptations when faced with new environments. In this paper, we propose a principled framework for adaptive RL, called \textit{AdaRL}, that adap…
Atari Gamesreinforcement-learningReinforcement Learning (RL)Transfer Reinforcement LearningLearning without Knowing: Unobserved Context in Continuous Transfer Reinforcement Learning
In this paper, we consider a transfer Reinforcement Learning (RL) problem in continuous state and action spaces, under unobserved contextual information. For example, the context can represent the mental view of the worl…
Autonomous DrivingImitation Learningreinforcement-learningReinforcement Learning+2Procedural Content Generation: Better Benchmarks for Transfer Reinforcement Learning
The idea of transfer in reinforcement learning (TRL) is intriguing: being able to transfer knowledge from one problem to another problem without learning everything from scratch. This promises quicker learning and learni…
BenchmarkingDeep Learningreinforcement-learningReinforcement Learning+2Scalable Multiagent Driving Policies For Reducing Traffic Congestion
Traffic congestion is a major challenge in modern urban settings. The industry-wide development of autonomous and automated vehicles (AVs) motivates the question of how can AVs contribute to congestion reduction. Past re…
Transfer LearningTransfer Reinforcement LearningDomain Adaptation In Reinforcement Learning Via Latent Unified State Representation
Despite the recent success of deep reinforcement learning (RL), domain adaptation remains an open problem. Although the generalization ability of RL agents is critical for the real-world applicability of Deep RL, zero-sh…
Autonomous DrivingDeep Reinforcement LearningDomain AdaptationImage-to-Image Translation+5Action Priors for Large Action Spaces in Robotics
In robotics, it is often not possible to learn useful policies using pure model-free reinforcement learning without significant reward shaping or curriculum learning. As a consequence, many researchers rely on expert dem…
reinforcement-learningReinforcement Learning (RL)Robotic GraspingTransfer Learning+1