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 226–250 of 15113 papers

TitleStatusHype
AGILE: A Novel Reinforcement Learning Framework of LLM AgentsCode2
Evolving Reservoirs for Meta Reinforcement LearningCode2
A Review of Safe Reinforcement Learning: Methods, Theory and ApplicationsCode2
Equivariant Ensembles and Regularization for Reinforcement Learning in Map-based Path PlanningCode2
EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and BenchmarkingCode2
G1: Bootstrapping Perception and Reasoning Abilities of Vision-Language Model via Reinforcement LearningCode2
Emergent Tool Use From Multi-Agent AutocurriculaCode2
Enhancing Multi-Step Reasoning Abilities of Language Models through Direct Q-Function OptimizationCode2
ElegantRL-Podracer: Scalable and Elastic Library for Cloud-Native Deep Reinforcement LearningCode2
Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language ModelsCode2
Memory, Benchmark & Robots: A Benchmark for Solving Complex Tasks with Reinforcement LearningCode2
EfficientZero V2: Mastering Discrete and Continuous Control with Limited DataCode2
A Comparative Study of Algorithms for Intelligent Traffic Signal ControlCode2
Embodied-R: Collaborative Framework for Activating Embodied Spatial Reasoning in Foundation Models via Reinforcement LearningCode2
Model-agnostic and Scalable Counterfactual Explanations via Reinforcement LearningCode2
MOMAland: A Set of Benchmarks for Multi-Objective Multi-Agent Reinforcement LearningCode2
Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode2
Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language ModelsCode2
Aligning AI With Shared Human ValuesCode2
Efficient Online Reinforcement Learning Fine-Tuning Need Not Retain Offline DataCode2
Multi-Stage Manipulation with Demonstration-Augmented Reward, Policy, and World Model LearningCode2
Benchmarking Deep Reinforcement Learning for Continuous ControlCode2
Easy-to-Hard Generalization: Scalable Alignment Beyond Human SupervisionCode2
Benchmarking Potential Based Rewards for Learning Humanoid LocomotionCode2
Efficient Online Reinforcement Learning with Offline DataCode2
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Benchmark Results

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