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 901–925 of 15113 papers

TitleStatusHype
De novo PROTAC design using graph-based deep generative modelsCode1
Scalable Multi-Agent Reinforcement Learning through Intelligent Information AggregationCode1
Synthesis of separation processes with reinforcement learningCode1
Learning safety in model-based Reinforcement Learning using MPC and Gaussian ProcessesCode1
Causal Counterfactuals for Improving the Robustness of Reinforcement LearningCode1
Multi-Agent Reinforcement Learning for Adaptive Mesh RefinementCode1
Spatial-temporal recurrent reinforcement learning for autonomous shipsCode1
Online Control of Adaptive Large Neighborhood Search using Deep Reinforcement LearningCode1
Agent-Controller Representations: Principled Offline RL with Rich Exogenous InformationCode1
RLET: A Reinforcement Learning Based Approach for Explainable QA with Entailment TreesCode1
Self-Improving Safety Performance of Reinforcement Learning Based Driving with Black-Box Verification AlgorithmsCode1
BIMRL: Brain Inspired Meta Reinforcement LearningCode1
DeFIX: Detecting and Fixing Failure Scenarios with Reinforcement Learning in Imitation Learning Based Autonomous DrivingCode1
Language Control Diffusion: Efficiently Scaling through Space, Time, and TasksCode1
Provable Safe Reinforcement Learning with Binary FeedbackCode1
Low-Rank Modular Reinforcement Learning via Muscle SynergyCode1
ERL-Re^2: Efficient Evolutionary Reinforcement Learning with Shared State Representation and Individual Policy RepresentationCode1
Adaptive Behavior Cloning Regularization for Stable Offline-to-Online Reinforcement LearningCode1
Teal: Learning-Accelerated Optimization of WAN Traffic EngineeringCode1
Sim-to-Real via Sim-to-Seg: End-to-end Off-road Autonomous Driving Without Real DataCode1
Multi-Agent Path Finding via Tree LSTMCode1
Evaluating Long-Term Memory in 3D MazesCode1
Energy Pricing in P2P Energy Systems Using Reinforcement LearningCode1
Avalon: A Benchmark for RL Generalization Using Procedurally Generated WorldsCode1
Symbolic Distillation for Learned TCP Congestion ControlCode1
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Benchmark Results

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