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 3501–3525 of 15113 papers

TitleStatusHype
Analysis on Riemann Hypothesis with Cross Entropy Optimization and Reasoning—0
Grounded Curriculum Learning—0
Focus On What Matters: Separated Models For Visual-Based RL Generalization—0
Constrained Reinforcement Learning for Safe Heat Pump ControlCode0
Strongly-polynomial time and validation analysis of policy gradient methods—0
Learning to Bridge the Gap: Efficient Novelty Recovery with Planning and Reinforcement Learning—0
Generalizing Consistency Policy to Visual RL with Prioritized Proximal Experience Regularization—0
Enhancing Spectrum Efficiency in 6G Satellite Networks: A GAIL-Powered Policy Learning via Asynchronous Federated Inverse Reinforcement Learning—0
Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning—0
Climate Adaptation with Reinforcement Learning: Experiments with Flooding and Transportation in CopenhagenCode0
TemporalPaD: a reinforcement-learning framework for temporal feature representation and dimension reduction—0
Autoregressive Multi-trait Essay Scoring via Reinforcement Learning with Scoring-aware Multiple Rewards—0
LoopSR: Looping Sim-and-Real for Lifelong Policy Adaptation of Legged Robots—0
Optimizing Downlink C-NOMA Transmission with Movable Antennas: A DDPG-based Approach—0
Learning with Dynamics: Autonomous Regulation of UAV Based Communication Networks with Dynamic UAV Crew—0
Asynchronous Fractional Multi-Agent Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
Revisiting Space Mission Planning: A Reinforcement Learning-Guided Approach for Multi-Debris Rendezvous—0
OffRIPP: Offline RL-based Informative Path Planning—0
A random measure approach to reinforcement learning in continuous time—0
On-orbit Servicing for Spacecraft Collision Avoidance With Autonomous Decision Making—0
Spiders Based on Anxiety: How Reinforcement Learning Can Deliver Desired User Experience in Virtual Reality Personalized Arachnophobia TreatmentCode0
Offline and Distributional Reinforcement Learning for Radio Resource Management—0
Reinforcement Leaning for Infinite-Dimensional Systems—0
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm—0
Whole-body End-Effector Pose Tracking—0
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Benchmark Results

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