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 1051–1075 of 15113 papers

TitleStatusHype
RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?—0
Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardwareCode0
Advancing Language Model Reasoning through Reinforcement Learning and Inference ScalingCode2
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
RbRL2.0: Integrated Reward and Policy Learning for Rating-based Reinforcement Learning—0
Combining LLM decision and RL action selection to improve RL policy for adaptive interventions—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
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Benchmark Results

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