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 2961–2970 of 15113 papers

TitleStatusHype
FitLight: Federated Imitation Learning for Plug-and-Play Autonomous Traffic Signal Control—0
Hovering Flight of Soft-Actuated Insect-Scale Micro Aerial Vehicles using Deep Reinforcement Learning—0
Scaling Test-Time Compute Without Verification or RL is Suboptimal—0
Learning Plasma Dynamics and Robust Rampdown Trajectories with Predict-First Experiments at TCV—0
VLP: Vision-Language Preference Learning for Embodied Manipulation—0
Intersectional Fairness in Reinforcement Learning with Large State and Constraint Spaces—0
CAMEL: Continuous Action Masking Enabled by Large Language Models for Reinforcement Learning—0
Addressing Moral Uncertainty using Large Language Models for Ethical Decision-Making—0
Robot Deformable Object Manipulation via NMPC-generated Demonstrations in Deep Reinforcement Learning—0
Evaluating the Paperclip Maximizer: Are RL-Based Language Models More Likely to Pursue Instrumental Goals?Code0
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Benchmark Results

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