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 3076–3100 of 15113 papers

TitleStatusHype
Reinforcement learning Based Automated Design of Differential Evolution Algorithm for Black-box Optimization—0
Exploring the Technology Landscape through Topic Modeling, Expert Involvement, and Reinforcement Learning—0
MONA: Myopic Optimization with Non-myopic Approval Can Mitigate Multi-step Reward Hacking—0
To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for Agricultural Management Decisions with Reinforcement LearningCode0
AdaWM: Adaptive World Model based Planning for Autonomous Driving—0
Reinforcement Learning Constrained Beam Search for Parameter Optimization of Paper Drying Under Flexible Constraints—0
Extend Adversarial Policy Against Neural Machine Translation via Unknown Token—0
RL-RC-DoT: A Block-level RL agent for Task-Aware Video Compression—0
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
GREEN-CODE: Learning to Optimize Energy Efficiency in LLM-based Code GenerationCode0
Solving Finite-Horizon MDPs via Low-Rank Tensors—0
RE-POSE: Synergizing Reinforcement Learning-Based Partitioning and Offloading for Edge Object Detection—0
Towards Large Reasoning Models: A Survey of Reinforced Reasoning with Large Language Models—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
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
Hybrid Action Based Reinforcement Learning for Multi-Objective Compatible Autonomous Driving—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
Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline DataCode0
Combining LLM decision and RL action selection to improve RL policy for adaptive interventions—0
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Benchmark Results

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