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 8326–8350 of 15113 papers

TitleStatusHype
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
End-to-end grasping policies for human-in-the-loop robots via deep reinforcement learningCode0
ANT: Learning Accurate Network Throughput for Better Adaptive Video Streaming—0
Constraint-Guided Reinforcement Learning: Augmenting the Agent-Environment-InteractionCode1
A Deep Reinforcement Learning Approach for the Meal Delivery Problem—0
Graph Neural Network Reinforcement Learning for Autonomous Mobility-on-Demand SystemsCode1
DisCo RL: Distribution-Conditioned Reinforcement Learning for General-Purpose Policies—0
Safe Chance Constrained Reinforcement Learning for Batch Process ControlCode0
Reinforcement Learning using Guided Observability—0
Reset-Free Reinforcement Learning via Multi-Task Learning: Learning Dexterous Manipulation Behaviors without Human Intervention—0
A learning gap between neuroscience and reinforcement learningCode0
Formula RL: Deep Reinforcement Learning for Autonomous Racing using Telemetry Data—0
Independent Reinforcement Learning for Weakly Cooperative Multiagent Traffic Control ProblemCode1
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
Tackling Variabilities in Autonomous Driving—0
Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems—0
CVLight: Decentralized Learning for Adaptive Traffic Signal Control with Connected Vehicles—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
Scalable Synthesis of Verified Controllers in Deep Reinforcement Learning—0
Network-wide traffic signal control optimization using a multi-agent deep reinforcement learning—0
MBRL-Lib: A Modular Library for Model-based Reinforcement LearningCode2
Outcome-Driven Reinforcement Learning via Variational Inference—0
Model-predictive control and reinforcement learning in multi-energy system case studies—0
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Benchmark Results

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