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 2751–2775 of 15113 papers

TitleStatusHype
Improving Mixed-Criticality Scheduling with Reinforcement Learning—0
Decision SpikeFormer: Spike-Driven Transformer for Decision Making—0
Dexterous Manipulation through Imitation Learning: A Survey—0
Learning Dual-Arm Coordination for Grasping Large Flat Objects—0
Reasoning Under 1 Billion: Memory-Augmented Reinforcement Learning for Large Language ModelsCode0
Adapting World Models with Latent-State Dynamics Residuals—0
Inference-Time Scaling for Generalist Reward Modeling—0
Reinforcement Learning for Solving the Pricing Problem in Column Generation: Applications to Vehicle Routing—0
Integrating Human Knowledge Through Action Masking in Reinforcement Learning for Operations Research—0
MAD: A Magnitude And Direction Policy Parametrization for Stability Constrained Reinforcement LearningCode0
Probabilistic Curriculum Learning for Goal-Based Reinforcement Learning—0
De Novo Molecular Design Enabled by Direct Preference Optimization and Curriculum Learning—0
How Difficulty-Aware Staged Reinforcement Learning Enhances LLMs' Reasoning Capabilities: A Preliminary Experimental Study—0
Grounding Multimodal LLMs to Embodied Agents that Ask for Help with Reinforcement Learning—0
Value Iteration for Learning Concurrently Executable Robotic Control TasksCode0
Nuclear Microreactor Control with Deep Reinforcement Learning—0
Reinforcement Learning for Safe Autonomous Two Device Navigation of Cerebral Vessels in Mechanical Thrombectomy—0
Noise-based reward-modulated learning—0
Fair Dynamic Spectrum Access via Fully Decentralized Multi-Agent Reinforcement Learning—0
A Survey of Reinforcement Learning-Based Motion Planning for Autonomous Driving: Lessons Learned from a Driving Task Perspective—0
JudgeLRM: Large Reasoning Models as a Judge—0
Accelerating High-Efficiency Organic Photovoltaic Discovery via Pretrained Graph Neural Networks and Generative Reinforcement Learning—0
HACTS: a Human-As-Copilot Teleoperation System for Robot Learning—0
Handling Delay in Real-Time Reinforcement LearningCode0
A Systematic Decade Review of Trip Route Planning with Travel Time Estimation based on User Preferences and Behavior—0
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Benchmark Results

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