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

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Showing 1051–1100 of 15113 papers

TitleStatusHype
Advancing Language Model Reasoning through Reinforcement Learning and Inference ScalingCode2
Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardwareCode0
RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?—0
GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code GenerationCode0
Solving Finite-Horizon MDPs via Low-Rank Tensors—0
PixelBrax: Learning Continuous Control from Pixels End-to-End on the GPUCode0
From Explainability to Interpretability: Interpretable Policies in Reinforcement Learning Via Model Explanation—0
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models—0
RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection—0
Average-Reward Reinforcement Learning with Entropy Regularization—0
Reinforcement Learning-Enhanced Procedural Generation for Dynamic Narrative-Driven AR Experiences—0
Projection Implicit Q-Learning with Support Constraint for Offline Reinforcement Learning—0
Decision Transformers for RIS-Assisted Systems with Diffusion Model-Based Channel Acquisition—0
CHEQ-ing the Box: Safe Variable Impedance Learning for Robotic PolishingCode0
FDPP: Fine-tune Diffusion Policy with Human Preference—0
Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving—0
Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline DataCode0
Future-Conditioned Recommendations with Multi-Objective Controllable Decision Transformer—0
Combining LLM decision and RL action selection to improve RL policy for adaptive interventions—0
RbRL2.0: Integrated Reward and Policy Learning for Rating-based Reinforcement Learning—0
Average Reward Reinforcement Learning for Wireless Radio Resource Management—0
DRDT3: Diffusion-Refined Decision Test-Time Training Model—0
Pareto Set Learning for Multi-Objective Reinforcement Learning—0
An Empirical Study of Deep Reinforcement Learning in Continuing TasksCode0
Hierarchical Reinforcement Learning for Optimal Agent Grouping in Cooperative Systems—0
AlgoPilot: Fully Autonomous Program Synthesis Without Human-Written Programs—0
A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement LearningCode0
Smart Imitator: Learning from Imperfect Clinical DecisionsCode0
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing—0
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingCode1
Real-Time Integrated Dispatching and Idle Fleet Steering with Deep Reinforcement Learning for A Meal Delivery Platform—0
Investigating the Impact of Observation Space Design Choices On Training Reinforcement Learning Solutions for Spacecraft Problems—0
Diffusion Models for Smarter UAVs: Decision-Making and Modeling—0
LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models—0
Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning—0
Safe Reinforcement Learning with Minimal Supervision—0
Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement LearningCode0
Risk-averse policies for natural gas futures trading using distributional reinforcement learning—0
Run-and-tumble chemotaxis using reinforcement learning—0
Explainable Reinforcement Learning via Temporal Policy Decomposition—0
Digital Twin Aided Channel Estimation: Zone-Specific Subspace Prediction and CalibrationCode0
Learn A Flexible Exploration Model for Parameterized Action Markov Decision Processes—0
Interpretable Recognition of Fused Magnesium Furnace Working Conditions with Deep Convolutional Stochastic Configuration Networks—0
Co-Activation Graph Analysis of Safety-Verified and Explainable Deep Reinforcement Learning PoliciesCode1
Sim-to-Real Transfer for Mobile Robots with Reinforcement Learning: from NVIDIA Isaac Sim to Gazebo and Real ROS 2 RobotsCode2
AMM: Adaptive Modularized Reinforcement Model for Multi-city Traffic Signal Control—0
Representation Convergence: Mutual Distillation is Secretly a Form of RegularizationCode0
A New Interpretation of the Certainty-Equivalence Approach for PAC Reinforcement Learning with a Generative Model—0
SR-Reward: Taking The Path More Traveled—0
On the Statistical Complexity for Offline and Low-Adaptive Reinforcement Learning with Structures—0
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Benchmark Results

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