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 7151–7200 of 15113 papers

TitleStatusHype
Improving Generalization of Deep Reinforcement Learning-based TSP Solvers—0
Scalable Multi-Agent Reinforcement Learning for Residential Load Scheduling under Data Governance—0
Hierarchical Potential-based Reward Shaping from Task SpecificationsCode0
Adaptive control of a mechatronic system using constrained residual reinforcement learning—0
Deep Reinforcement Learning for Solving the Heterogeneous Capacitated Vehicle Routing ProblemCode1
Heterogeneous Attentions for Solving Pickup and Delivery Problem via Deep Reinforcement Learning—0
Decentralized Cooperative Lane Changing at Freeway Weaving Areas Using Multi-Agent Deep Reinforcement Learning—0
Deep reinforcement learning for guidewire navigation in coronary artery phantom—0
CARL: A Benchmark for Contextual and Adaptive Reinforcement LearningCode1
DeepEdge: A Deep Reinforcement Learning based Task Orchestrator for Edge Computing—0
A Deep Reinforcement Learning Framework for Contention-Based Spectrum Sharing—0
A study of first-passage time minimization via Q-learning in heated gridworlds—0
Dropout Q-Functions for Doubly Efficient Reinforcement LearningCode1
OTTR: Off-Road Trajectory Tracking using Reinforcement Learning—0
NaRLE: Natural Language Models using Reinforcement Learning with Emotion Feedback—0
Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design—0
You Only Evaluate Once: a Simple Baseline Algorithm for Offline RL—0
Multi-Agent Path Planning Using Deep Reinforcement Learning—0
Uncertainty-Based Offline Reinforcement Learning with Diversified Q-EnsembleCode1
Reinforcement Learning for Admission Control in Wireless Virtual Network Embedding—0
Large Batch Experience ReplayCode1
Behaviour-conditioned policies for cooperative reinforcement learning tasks—0
A Modified Q-Learning Algorithm for Rate-Profiling of Polarization Adjusted Convolutional (PAC) Codes—0
Learning to Assist Agents by Observing Them—0
Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation—0
Collective eXplainable AI: Explaining Cooperative Strategies and Agent Contribution in Multiagent Reinforcement Learning with Shapley ValuesCode1
Automating Privilege Escalation with Deep Reinforcement Learning—0
DRL-Clusters: Buffer Management with Clustering based Deep Reinforcement Learning—0
Meta-Reinforcement Learning via Buffering Graph Signatures for Live Video Streaming EventsCode0
Decentralized Safe Reinforcement Learning for Voltage Control—0
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances—0
Parallel Actors and Learners: A Framework for Generating Scalable RL Implementations—0
Mapping Language to Programs using Multiple Reward Components with Inverse Reinforcement LearningCode0
Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
BRAC+: Improved Behavior Regularized Actor Critic for Offline Reinforcement LearningCode0
Terminal Adaptive Guidance for Autonomous Hypersonic Strike Weapons via Reinforcement Learning—0
Offline Reinforcement Learning with Reverse Model-based ImaginationCode1
Safety aware model-based reinforcement learning for optimal control of a class of output-feedback nonlinear systems—0
Motion Planning for Autonomous Vehicles in the Presence of Uncertainty Using Reinforcement Learning—0
Multi-lane Cruising Using Hierarchical Planning and Reinforcement Learning—0
A Cramér Distance perspective on Quantile Regression based Distributional Reinforcement LearningCode0
Cellular traffic offloading via Opportunistic Networking with Reinforcement Learning—0
Guiding Evolutionary Strategies by Differentiable Robot SimulatorsCode0
DNN-Opt: An RL Inspired Optimization for Analog Circuit Sizing using Deep Neural Networks—0
Divergence-Regularized Multi-Agent Actor-Critic—0
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
Neural Network Verification in Control—0
MOLUCINATE: A Generative Model for Molecules in 3D SpaceCode1
Trajectory Planning with Deep Reinforcement Learning in High-Level Action Spaces—0
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Benchmark Results

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