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 301–325 of 15113 papers

TitleStatusHype
Aligning AI With Shared Human ValuesCode2
Feedback Efficient Online Fine-Tuning of Diffusion ModelsCode2
Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionCode2
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement LearningCode2
Agent models: Internalizing Chain-of-Action Generation into Reasoning modelsCode2
Diffusion-based Reinforcement Learning via Q-weighted Variational Policy OptimizationCode2
Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem SolvingCode2
FlowReasoner: Reinforcing Query-Level Meta-AgentsCode2
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex ManipulationCode2
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement LearningCode2
Diffusion Models for Reinforcement Learning: A SurveyCode2
Generative Auto-Bidding with Value-Guided ExplorationsCode2
Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and MemoryCode2
Godot Reinforcement Learning AgentsCode2
Digi-Q: Learning Q-Value Functions for Training Device-Control AgentsCode2
DiffMimic: Efficient Motion Mimicking with Differentiable PhysicsCode2
DIAMBRA Arena: a New Reinforcement Learning Platform for Research and ExperimentationCode2
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement LearningCode2
Dialogue Learning With Human-In-The-LoopCode2
Heterogeneous Multi-Robot Reinforcement LearningCode2
Diffusion Actor-Critic with Entropy RegulatorCode2
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement LearningCode2
Direct Multi-Turn Preference Optimization for Language AgentsCode2
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline DataCode2
Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical RobotCode2
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Benchmark Results

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