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 301–350 of 15113 papers

TitleStatusHype
Exploring the Limit of Outcome Reward for Learning Mathematical ReasoningCode2
DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control in the IoVCode2
Direct Multi-Turn Preference Optimization for Language AgentsCode2
Digi-Q: Learning Q-Value Functions for Training Device-Control AgentsCode2
A Critical Evaluation of AI Feedback for Aligning Large Language ModelsCode2
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement LearningCode2
Distributional Soft Actor-Critic with Three RefinementsCode2
Foundation Policies with Hilbert RepresentationsCode2
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex ManipulationCode2
Assessment of Reinforcement Learning for Macro PlacementCode2
Diffusion Actor-Critic with Entropy RegulatorCode2
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, ChallengesCode2
A Walk in the Park: Learning to Walk in 20 Minutes With Model-Free Reinforcement LearningCode2
RLtools: A Fast, Portable Deep Reinforcement Learning Library for Continuous ControlCode2
Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and MemoryCode2
A Simulation Benchmark for Autonomous Racing with Large-Scale Human DataCode2
Gradient Boosting Reinforcement LearningCode2
DiffMimic: Efficient Motion Mimicking with Differentiable PhysicsCode2
Habitat 2.0: Training Home Assistants to Rearrange their HabitatCode2
Heterogeneous Multi-Robot Reinforcement LearningCode2
Aligning AI With Shared Human ValuesCode2
Diffusion-based Reinforcement Learning via Q-weighted Variational Policy OptimizationCode2
Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk ManagementCode2
Agent RL Scaling Law: Agent RL with Spontaneous Code Execution for Mathematical Problem SolvingCode2
Agent models: Internalizing Chain-of-Action Generation into Reasoning modelsCode2
iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvementCode2
Dialogue Learning With Human-In-The-LoopCode2
In-Hand Object Rotation via Rapid Motor AdaptationCode2
Integrating Reinforcement Learning with Foundation Models for Autonomous Robotics: Methods and PerspectivesCode2
A Cooperation Graph Approach for Multiagent Sparse Reward Reinforcement LearningCode2
AGILE: A Novel Reinforcement Learning Framework of LLM AgentsCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
Jack of All Trades, Master of Some, a Multi-Purpose Transformer AgentCode2
DIAMBRA Arena: a New Reinforcement Learning Platform for Research and ExperimentationCode2
Diffusion Models for Reinforcement Learning: A SurveyCode2
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
Beyond 'Aha!': Toward Systematic Meta-Abilities Alignment in Large Reasoning ModelsCode2
Language Models can Solve Computer TasksCode2
LLMLight: Large Language Models as Traffic Signal Control AgentsCode2
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
Big-Math: A Large-Scale, High-Quality Math Dataset for Reinforcement Learning in Language ModelsCode2
Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter ControlCode2
Learning to Predict Without Looking Ahead: World Models Without Forward PredictionCode2
ARPO:End-to-End Policy Optimization for GUI Agents with Experience ReplayCode2
Deep Reinforcement Learning for Multi-Agent InteractionCode2
DayDreamer: World Models for Physical Robot LearningCode2
D4RL: Datasets for Deep Data-Driven Reinforcement LearningCode2
Decoupling Representation Learning from Reinforcement LearningCode2
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Benchmark Results

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