SOTAVerified

Reinforcement Learning (RL)

Reinforcement Learning (RL) involves training an agent to take actions in an environment to maximize a cumulative reward signal. The agent interacts with the environment and learns by receiving feedback in the form of rewards or punishments for its actions. The goal of reinforcement learning is to find the optimal policy or decision-making strategy that maximizes the long-term reward.

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2176–2200 of 15113 papers

TitleStatusHype
Multi-view Disentanglement for Reinforcement Learning with Multiple CamerasCode0
Beyond the Edge: An Advanced Exploration of Reinforcement Learning for Mobile Edge Computing, its Applications, and Future Research Trajectories—0
An Offline Reinforcement Learning Algorithm Customized for Multi-Task Fusion in Large-Scale Recommender Systems—0
Data-Incremental Continual Offline Reinforcement Learning—0
Reinforcement Learning Approach for Integrating Compressed Contexts into Knowledge Graphs—0
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Continuous-time Risk-sensitive Reinforcement Learning via Quadratic Variation Penalty—0
TrajDeleter: Enabling Trajectory Forgetting in Offline Reinforcement Learning AgentsCode0
Actor-Critic Reinforcement Learning with Phased Actor—0
Physics-informed Actor-Critic for Coordination of Virtual Inertia from Power Distribution Systems—0
Prompt Optimizer of Text-to-Image Diffusion Models for Abstract Concept Understanding—0
Learn to Tour: Operator Design For Solution Feasibility Mapping in Pickup-and-delivery Traveling Salesman Problem—0
LTL-Constrained Policy Optimization with Cycle Experience Replay—0
Sustainability of Data Center Digital Twins with Reinforcement LearningCode2
Course Recommender Systems Need to Consider the Job MarketCode0
Automated Discovery of Functional Actual Causes in Complex Environments—0
What Hides behind Unfairness? Exploring Dynamics Fairness in Reinforcement LearningCode0
Achieving Constant Regret in Linear Markov Decision Processes—0
Offline Trajectory Generalization for Offline Reinforcement Learning—0
Simplex Decomposition for Portfolio Allocation Constraints in Reinforcement Learning—0
The Feasibility of Constrained Reinforcement Learning Algorithms: A Tutorial Study—0
Autonomous Path Planning for Intercostal Robotic Ultrasound Imaging Using Reinforcement Learning—0
Effective Reinforcement Learning Based on Structural Information Principles—0
Inferring Behavior-Specific Context Improves Zero-Shot Generalization in Reinforcement LearningCode0
Higher Replay Ratio Empowers Sample-Efficient Multi-Agent Reinforcement Learning—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1PPGMean Normalized Performance0.76—Unverified
2PPOMean Normalized Performance0.58—Unverified