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 326–350 of 15113 papers

TitleStatusHype
HumanOmniV2: From Understanding to Omni-Modal Reasoning with ContextCode2
Dialogue Learning With Human-In-The-LoopCode2
In-Hand Object Rotation via Rapid Motor AdaptationCode2
A Simulation Benchmark for Autonomous Racing with Large-Scale Human DataCode2
Interactive Differentiable SimulationCode2
Diffusion Actor-Critic with Entropy RegulatorCode2
Direct Multi-Turn Preference Optimization for Language AgentsCode2
Jack of All Trades, Master of Some, a Multi-Purpose Transformer AgentCode2
JaxMARL: Multi-Agent RL Environments and Algorithms in JAXCode2
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline DataCode2
Kinetix: Investigating the Training of General Agents through Open-Ended Physics-Based Control TasksCode2
A Toolkit for Reliable Benchmarking and Research in Multi-Objective Reinforcement LearningCode2
Language Models can Solve Computer TasksCode2
Demonstration-Guided Reinforcement Learning with Efficient Exploration for Task Automation of Surgical RobotCode2
Deep Reinforcement Learning for Multi-Agent InteractionCode2
Learning Heterogeneous Agent Cooperation via Multiagent League TrainingCode2
Learning Physically Realizable Skills for Online Packing of General 3D ShapesCode2
Advancing Language Model Reasoning through Reinforcement Learning and Inference ScalingCode2
Deep Reinforcement Learning in a Handful of Trials using Probabilistic Dynamics ModelsCode2
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest QuestionsCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
Datasets and Benchmarks for Offline Safe Reinforcement LearningCode2
D4RL: Datasets for Deep Data-Driven Reinforcement LearningCode2
CTR-Driven Advertising Image Generation with Multimodal Large Language ModelsCode2
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Benchmark Results

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