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 1176–1200 of 15113 papers

TitleStatusHype
Physics Instrument Design with Reinforcement Learning—0
PickLLM: Context-Aware RL-Assisted Large Language Model Routing—0
From Text to Trajectory: Exploring Complex Constraint Representation and Decomposition in Safe Reinforcement Learning—0
Reinforcement Learning Within the Classical Robotics Stack: A Case Study in Robot Soccer—0
Quantum-Train-Based Distributed Multi-Agent Reinforcement Learning—0
Radiology Report Generation via Multi-objective Preference Optimization—0
Latent Safety-Constrained Policy Approach for Safe Offline Reinforcement LearningCode0
Coarse-to-Fine: A Dual-Phase Channel-Adaptive Method for Wireless Image Transmission—0
SINERGYM -- A virtual testbed for building energy optimization with Reinforcement LearningCode3
Ask1: Development and Reinforcement Learning-Based Control of a Custom Quadruped Robot—0
Preference Adaptive and Sequential Text-to-Image Generation—0
Optimizing Sensor Redundancy in Sequential Decision-Making Problems—0
Mobile-TeleVision: Predictive Motion Priors for Humanoid Whole-Body Control—0
Reinforcement Learning Policy as Macro Regulator Rather than Macro PlacerCode1
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline DataCode2
Progressive-Resolution Policy Distillation: Leveraging Coarse-Resolution Simulations for Time-Efficient Fine-Resolution Policy Learning—0
Swarm Behavior Cloning—0
ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home Rearrangement TasksCode2
Skill-Enhanced Reinforcement Learning Acceleration from Demonstrations—0
Policy Agnostic RL: Offline RL and Online RL Fine-Tuning of Any Class and Backbone—0
Unraveling the Complexity of Memory in RL Agents: an Approach for Classification and Evaluation—0
Mean--Variance Portfolio Selection by Continuous-Time Reinforcement Learning: Algorithms, Regret Analysis, and Empirical Study—0
Reinforcement Learning for a Discrete-Time Linear-Quadratic Control Problem with an Application—0
M^3PC: Test-time Model Predictive Control for Pretrained Masked Trajectory ModelCode1
Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories—0
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Benchmark Results

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