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 6676–6700 of 15113 papers

TitleStatusHype
DRL-based Slice Placement Under Non-Stationary Conditions—0
DRL-based Slice Placement under Realistic Network Load Conditions—0
DRL-Clusters: Buffer Management with Clustering based Deep Reinforcement Learning—0
Beyond Sparse Rewards: Enhancing Reinforcement Learning with Language Model Critique in Text Generation—0
DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future—0
DRL-FAS: A Novel Framework Based on Deep Reinforcement Learning for Face Anti-Spoofing—0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
DR-MPC: Deep Residual Model Predictive Control for Real-world Social Navigation—0
DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning—0
DSADF: Thinking Fast and Slow for Decision Making—0
DSDF: An approach to handle stochastic agents in collaborative multi-agent reinforcement learning—0
DSDF: Coordinated look-ahead strategy in stochastic multi-agent reinforcement learning—0
D-Shape: Demonstration-Shaped Reinforcement Learning via Goal Conditioning—0
DSP: A Differential Spatial Prediction Scheme for Comprehensive real industrial datasets—0
Dual Active Learning for Reinforcement Learning from Human Feedback—0
Dual-Agent Deep Reinforcement Learning for Deformable Face Tracking—0
Dual Behavior Regularized Reinforcement Learning—0
Dual Control for Approximate Bayesian Reinforcement Learning—0
Dual Ensemble Kalman Filter for Stochastic Optimal Control—0
Dual Generator Offline Reinforcement Learning—0
Dual-Objective Reinforcement Learning with Novel Hamilton-Jacobi-Bellman Formulations—0
Dueling Deep Q Network for Highway Decision Making in Autonomous Vehicles: A Case Study—0
Dueling RL: Reinforcement Learning with Trajectory Preferences—0
DyFEn: Agent-Based Fee Setting in Payment Channel Networks—0
Dynamical Distance Learning for Semi-Supervised and Unsupervised Skill Discovery—0
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Benchmark Results

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