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 1926–1950 of 15113 papers

TitleStatusHype
Backprop-Free Reinforcement Learning with Active Neural Generative CodingCode1
Bidirectional Model-based Policy OptimizationCode1
Guiding Online Reinforcement Learning with Action-Free Offline PretrainingCode1
Scaling Offline Model-Based RL via Jointly-Optimized World-Action Model PretrainingCode1
ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learningCode1
Adaptive Risk-Tendency: Nano Drone Navigation in Cluttered Environments with Distributional Reinforcement LearningCode1
Addressing Function Approximation Error in Actor-Critic MethodsCode1
Beyond Greedy Search: Tracking by Multi-Agent Reinforcement Learning-based Beam SearchCode1
A Search-Based Testing Approach for Deep Reinforcement Learning AgentsCode1
GUI-G1: Understanding R1-Zero-Like Training for Visual Grounding in GUI AgentsCode1
Gym-ANM: Reinforcement Learning Environments for Active Network Management Tasks in Electricity Distribution SystemsCode1
Beyond OOD State Actions: Supported Cross-Domain Offline Reinforcement LearningCode1
Hearts Gym: Learning Reinforcement Learning as a Team EventCode1
Beyond Pick-and-Place: Tackling Robotic Stacking of Diverse ShapesCode1
Improving Model-Based Reinforcement Learning with Internal State Representations through Self-SupervisionCode1
Self-Activating Neural Ensembles for Continual Reinforcement LearningCode1
Improving the Validity of Automatically Generated Feedback via Reinforcement LearningCode1
Harnessing Mixed Offline Reinforcement Learning Datasets via Trajectory WeightingCode1
Self-Paced Contextual Reinforcement LearningCode1
Information Design in Multi-Agent Reinforcement LearningCode1
Harnessing Discrete Representations For Continual Reinforcement LearningCode1
A multi-agent reinforcement learning model of common-pool resource appropriationCode1
Self-Supervised Discovering of Interpretable Features for Reinforcement LearningCode1
HAZARD Challenge: Embodied Decision Making in Dynamically Changing EnvironmentsCode1
Optimization Methods for Interpretable Differentiable Decision Trees in Reinforcement LearningCode1
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Benchmark Results

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