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 7451–7475 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
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Benchmark Results

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