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

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Showing 7401–7450 of 15113 papers

TitleStatusHype
Combining Contention-Based Spectrum Access and Adaptive Modulation using Deep Reinforcement Learning—0
Learning to Walk in Minutes Using Massively Parallel Deep Reinforcement LearningCode2
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems—0
Parameter-free Reduction of the Estimation Bias in Deep Reinforcement Learning for Deterministic Policy GradientsCode0
Regularization Guarantees Generalization in Bayesian Reinforcement Learning through Algorithmic Stability—0
Pythia: A Customizable Hardware Prefetching Framework Using Online Reinforcement LearningCode1
NICE: Robust Scheduling through Reinforcement Learning-Guided Integer ProgrammingCode1
Reinforcement Learning Under Algorithmic Triage—0
Trust Region Policy Optimisation in Multi-Agent Reinforcement LearningCode1
PredictionNet: Real-Time Joint Probabilistic Traffic Prediction for Planning, Control, and Simulation—0
The Role of Tactile Sensing in Learning and Deploying Grasp Refinement AlgorithmsCode1
Hierarchies of Planning and Reinforcement Learning for Robot Navigation—0
Enhancing Navigational Safety in Crowded Environments using Semantic-Deep-Reinforcement-Learning-based NavigationCode1
Dimension-Free Rates for Natural Policy Gradient in Multi-Agent Reinforcement Learning—0
A Multi-Agent Deep Reinforcement Learning Coordination Framework for Connected and Automated Vehicles at Merging Roadways—0
Deep Reinforcement Learning-Based Long-Range Autonomous Valet Parking for Smart Cities—0
Adversarial Training Blocks Generalization in Neural Policies—0
Return Dispersion as an Estimator of Learning Potential for Prioritized Level Replay—0
ENERO: Efficient Real-Time WAN Routing Optimization with Deep Reinforcement LearningCode1
A Workflow for Offline Model-Free Robotic Reinforcement LearningCode1
A Survey on Reinforcement Learning for Recommender Systems—0
Benchmarking Lane-changing Decision-making for Deep Reinforcement Learning—0
Estimation Error Correction in Deep Reinforcement Learning for Deterministic Actor-Critic MethodsCode0
A Reinforcement Learning Benchmark for Autonomous Driving in Intersection ScenariosCode1
Introducing Symmetries to Black Box Meta Reinforcement Learning—0
Towards Multi-Agent Reinforcement Learning using Quantum Boltzmann Machines—0
MEPG: A Minimalist Ensemble Policy Gradient Framework for Deep Reinforcement Learning—0
Locality Matters: A Scalable Value Decomposition Approach for Cooperative Multi-Agent Reinforcement Learning—0
Long-Term Exploration in Persistent MDPsCode0
Generalization in Text-based Games via Hierarchical Reinforcement LearningCode0
Example-Driven Model-Based Reinforcement Learning for Solving Long-Horizon Visuomotor Tasks—0
A Distance-based Anomaly Detection Framework for Deep Reinforcement Learning—0
AutoPhoto: Aesthetic Photo Capture using Reinforcement LearningCode1
Learning offline: memory replay in biological and artificial reinforcement learning—0
Deep Policies for Online Bipartite Matching: A Reinforcement Learning ApproachCode1
A Reinforcement Learning Approach to the Stochastic Cutting Stock Problem—0
ACReL: Adversarial Conditional value-at-risk Reinforcement Learning—0
Reinforcement Learning for Finite-Horizon Restless Multi-Armed Multi-Action Bandits—0
Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play—0
Learning Natural Language Generation from Scratch—0
A Survey of Text Games for Reinforcement Learning informed by Natural Language—0
Dual Behavior Regularized Reinforcement Learning—0
Greedy UnMixing for Q-Learning in Multi-Agent Reinforcement Learning—0
Regularize! Don't Mix: Multi-Agent Reinforcement Learning without Explicit Centralized Structures—0
Lifelong Robotic Reinforcement Learning by Retaining Experiences—0
Hindsight Foresight Relabeling for Meta-Reinforcement LearningCode0
Deep Reinforcement Learning Based Multidimensional Resource Management for Energy Harvesting Cognitive NOMA Communications—0
Exploring the Training Robustness of Distributional Reinforcement Learning against Noisy State ObservationsCode0
POAR: Efficient Policy Optimization via Online Abstract State Representation Learning—0
Accelerating Offline Reinforcement Learning Application in Real-Time Bidding and Recommendation: Potential Use of Simulation—0
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Benchmark Results

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