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 9126–9150 of 15113 papers

TitleStatusHype
CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles—0
Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems—0
Tackling Variabilities in Autonomous Driving—0
Policy Fusion for Adaptive and Customizable Reinforcement Learning Agents—0
Model-aided Deep Reinforcement Learning for Sample-efficient UAV Trajectory Design in IoT Networks—0
Model-predictive control and reinforcement learning in multi-energy system case studies—0
Network Defense is Not a Game—0
Outcome-Driven Reinforcement Learning via Variational Inference—0
Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning—0
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning—0
Prospective Artificial Intelligence Approaches for Active Cyber Defence—0
DRL: Deep Reinforcement Learning for Intelligent Robot Control -- Concept, Literature, and Future—0
Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning—0
GLiDE: Generalizable Quadrupedal Locomotion in Diverse Environments with a Centroidal Model—0
Agent-Centric Representations for Multi-Agent Reinforcement Learning—0
Constraints Satisfiability Driven Reinforcement Learning for Autonomous Cyber Defense—0
Approximated Multi-Agent Fitted Q Iteration—0
Adaptive learning for financial markets mixing model-based and model-free RL for volatility targeting—0
Deep Reinforcement Learning in a Monetary Model—0
Training Value-Aligned Reinforcement Learning Agents Using a Normative Prior—0
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement LearningCode0
Singular Perturbation-based Reinforcement Learning of Two-Point Boundary Optimal Control Systems—0
Reinforcement learning for linear-convex models with jumps via stability analysis of feedback controls—0
Quick Learner Automated Vehicle Adapting its Roadmanship to Varying Traffic Cultures with Meta Reinforcement Learning—0
Reinforcement learning based process optimization and strategy development in conventional tunnelingCode0
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Benchmark Results

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