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 826–850 of 15113 papers

TitleStatusHype
Towards Autonomous Reinforcement Learning for Real-World Robotic Manipulation with Large Language Models—0
Data-Efficient Learning from Human Interventions for Mobile Robots—0
Rebalanced Multimodal Learning with Data-aware Unimodal Sampling—0
DreamerV3 for Traffic Signal Control: Hyperparameter Tuning and Performance—0
Rewarding Doubt: A Reinforcement Learning Approach to Confidence Calibration of Large Language Models—0
Quantitative Resilience Modeling for Autonomous Cyber Defense—0
Accelerating Multi-Task Temporal Difference Learning under Low-Rank Representation—0
What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret—0
All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning—0
Active Alignments of Lens Systems with Reinforcement Learning—0
Cognitive Behaviors that Enable Self-Improving Reasoners, or, Four Habits of Highly Effective STaRsCode3
Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning—0
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model LearningCode2
Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models—0
Minimax Optimal Reinforcement Learning with Quasi-Optimism—0
Reinforcement learning with combinatorial actions for coupled restless banditsCode1
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process—0
Scalable Reinforcement Learning for Virtual Machine Scheduling—0
Discrete Codebook World Models for Continuous ControlCode1
Never too Prim to Swim: An LLM-Enhanced RL-based Adaptive S-Surface Controller for AUVs under Extreme Sea Conditions—0
What Makes a Good Diffusion Planner for Decision Making?Code2
Adaptive Reinforcement Learning for State Avoidance in Discrete Event Systems—0
DeepRetrieval: Hacking Real Search Engines and Retrievers with Large Language Models via Reinforcement LearningCode4
Subtask-Aware Visual Reward Learning from Segmented Demonstrations—0
Hierarchical and Modular Network on Non-prehensile Manipulation in General Environments—0
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Benchmark Results

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