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 10951–10975 of 15113 papers

TitleStatusHype
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
Show:102550
← PrevPage 439 of 605Next →

Benchmark Results

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