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 251–300 of 15113 papers

TitleStatusHype
Ghost in the Minecraft: Generally Capable Agents for Open-World Environments via Large Language Models with Text-based Knowledge and MemoryCode2
REBEL: Reinforcement Learning via Regressing Relative RewardsCode2
Advancing Language Model Reasoning through Reinforcement Learning and Inference ScalingCode2
Generalized Inner Loop Meta-LearningCode2
FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex ManipulationCode2
Foundation Policies with Hilbert RepresentationsCode2
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement LearningCode2
Smooth Exploration for Robotic Reinforcement LearningCode2
Godot Reinforcement Learning AgentsCode2
Honor of Kings Arena: an Environment for Generalization in Competitive Reinforcement LearningCode2
FlagVNE: A Flexible and Generalizable Reinforcement Learning Framework for Network Resource AllocationCode2
Flightmare: A Flexible Quadrotor SimulatorCode2
FinRL-Meta: A Universe of Near-Real Market Environments for Data-Driven Deep Reinforcement Learning in Quantitative FinanceCode2
Flow: A Modular Learning Framework for Mixed Autonomy TrafficCode2
Fiber: A Platform for Efficient Development and Distributed Training for Reinforcement Learning and Population-Based MethodsCode2
Feedback Efficient Online Fine-Tuning of Diffusion ModelsCode2
Fine-Tuning Discrete Diffusion Models via Reward Optimization with Applications to DNA and Protein DesignCode2
A Tutorial on Bayesian Optimization of Expensive Cost Functions, with Application to Active User Modeling and Hierarchical Reinforcement LearningCode2
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement LearningCode2
EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and BenchmarkingCode2
Evolving Reservoirs for Meta Reinforcement LearningCode2
A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, ChallengesCode2
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path PlanningCode2
Exploring the Effect of Reinforcement Learning on Video Understanding: Insights from SEED-Bench-R1Code2
Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement LearningCode2
EfficientZero V2: Mastering Discrete and Continuous Control with Limited DataCode2
Emergent Tool Use From Multi-Agent AutocurriculaCode2
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline DataCode2
EasyRL4Rec: An Easy-to-use Library for Reinforcement Learning Based Recommender SystemsCode2
DynamicRAG: Leveraging Outputs of Large Language Model as Feedback for Dynamic Reranking in Retrieval-Augmented GenerationCode2
Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionCode2
Efficient Online Reinforcement Learning with Offline DataCode2
Exploring the Limit of Outcome Reward for Learning Mathematical ReasoningCode2
DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical DialogueCode2
Direct Multi-Turn Preference Optimization for Language AgentsCode2
Diffusion Policies as an Expressive Policy Class for Offline Reinforcement LearningCode2
Diffusion Models for Reinforcement Learning: A SurveyCode2
Accelerated Methods for Deep Reinforcement LearningCode2
ARPO:End-to-End Policy Optimization for GUI Agents with Experience ReplayCode2
Assessment of Reinforcement Learning for Macro PlacementCode2
Efficient World Models with Context-Aware TokenizationCode2
A Simulation Benchmark for Autonomous Racing with Large-Scale Human DataCode2
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement LearningCode2
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function OptimizationCode2
Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language ModelsCode2
A Critical Evaluation of AI Feedback for Aligning Large Language ModelsCode2
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
Diffusion-based Reinforcement Learning via Q-weighted Variational Policy OptimizationCode2
Digi-Q: Learning Q-Value Functions for Training Device-Control AgentsCode2
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Benchmark Results

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