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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 7476–7500 of 15113 papers

TitleStatusHype
Formulation of Deep Reinforcement Learning Architecture Toward Autonomous Driving for On-Ramp Merge—0
For Pre-Trained Vision Models in Motor Control, Not All Policy Learning Methods are Created Equal—0
Fortune: Formula-Driven Reinforcement Learning for Symbolic Table Reasoning in Language Models—0
Forward-Backward Reinforcement Learning—0
Forward KL Regularized Preference Optimization for Aligning Diffusion Policies—0
FOSP: Fine-tuning Offline Safe Policy through World Models—0
Foundation Models for Semantic Novelty in Reinforcement Learning—0
Reinforcement Learning with Foundation Priors: Let the Embodied Agent Efficiently Learn on Its Own—0
Foundations for Transfer in Reinforcement Learning: A Taxonomy of Knowledge Modalities—0
Foundations of Multivariate Distributional Reinforcement Learning—0
Fourier Policy Gradients—0
Fox in the Henhouse: Supply-Chain Backdoor Attacks Against Reinforcement Learning—0
FPGA-Based Neural Thrust Controller for UAVs—0
FPGA Divide-and-Conquer Placement using Deep Reinforcement Learning—0
f-Policy Gradients: A General Framework for Goal Conditioned RL using f-Divergences—0
Fractal Landscapes in Policy Optimization—0
Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
Fractional Transfer Learning for Deep Model-Based Reinforcement Learning—0
Fragment-based Sequential Translation for Molecular Optimization—0
FrameHopper: Selective Processing of Video Frames in Detection-driven Real-Time Video Analytics—0
Framework of Automatic Text Summarization Using Reinforcement Learning—0
Free^2Guide: Gradient-Free Path Integral Control for Enhancing Text-to-Video Generation with Large Vision-Language Models—0
Free Energy Projective Simulation (FEPS): Active inference with interpretability—0
FreeKD: Free-direction Knowledge Distillation for Graph Neural Networks—0
Freeway Merging in Congested Traffic based on Multipolicy Decision Making with Passive Actor Critic—0
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Benchmark Results

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