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 7451–7500 of 15113 papers

TitleStatusHype
FlexPool: A Distributed Model-Free Deep Reinforcement Learning Algorithm for Joint Passengers & Goods Transportation—0
Flipping-based Policy for Chance-Constrained Markov Decision Processes—0
Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning—0
Flow Navigation by Smart Microswimmers via Reinforcement Learning—0
Flow Rate Control in Smart District Heating Systems Using Deep Reinforcement Learning—0
Flow Shape Design for Microfluidic Devices Using Deep Reinforcement Learning—0
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks—0
Flow to Control: Offline Reinforcement Learning with Lossless Primitive Discovery—0
Floyd-Warshall Reinforcement Learning: Learning from Past Experiences to Reach New Goals—0
Fly, Fail, Fix: Iterative Game Repair with Reinforcement Learning and Large Multimodal Models—0
FNAS: Uncertainty-Aware Fast Neural Architecture Search—0
Focus On What Matters: Separated Models For Visual-Based RL Generalization—0
FoldingZero: Protein Folding from Scratch in Hydrophobic-Polar Model—0
Following Instructions by Imagining and Reaching Visual Goals—0
FollowNet: Robot Navigation by Following Natural Language Directions with Deep Reinforcement Learning—0
Follow the Soldiers with Optimized Single-Shot Multibox Detection and Reinforcement Learning—0
Follow your Nose: Using General Value Functions for Directed Exploration in Reinforcement Learning—0
Forecaster-aided User Association and Load Balancing in Multi-band Mobile Networks—0
Foresight of Graph Reinforcement Learning Latent Permutations Learnt by Gumbel Sinkhorn Network—0
Forethought and Hindsight in Credit Assignment—0
Formal Controller Synthesis for Continuous-Space MDPs via Model-Free Reinforcement Learning—0
Formalising the Foundations of Discrete Reinforcement Learning in Isabelle/HOL—0
Formal Policy Synthesis for Continuous-Space Systems via Reinforcement Learning—0
Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data—0
Formulation and validation of a car-following model based on deep reinforcement learning—0
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