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 3121–3130 of 15113 papers

TitleStatusHype
Unbiased Weight Maximization—0
ExWarp: Extrapolation and Warping-based Temporal Supersampling for High-frequency Displays—0
Theoretically Guaranteed Policy Improvement Distilled from Model-Based Planning—0
On the Effectiveness of Offline RL for Dialogue Response GenerationCode0
Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal ControlCode1
HIQL: Offline Goal-Conditioned RL with Latent States as ActionsCode1
Game-Theoretic Robust Reinforcement Learning Handles Temporally-Coupled Perturbations—0
DIP-RL: Demonstration-Inferred Preference Learning in Minecraft—0
Offline Multi-Agent Reinforcement Learning with Implicit Global-to-Local Value RegularizationCode1
JoinGym: An Efficient Query Optimization Environment for Reinforcement LearningCode1
Show:102550
← PrevPage 313 of 1512Next →

Benchmark Results

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