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 9151–9200 of 15113 papers

TitleStatusHype
Learning Vehicle Routing Problems using Policy Optimisation—0
SPOTTER: Extending Symbolic Planning Operators through Targeted Reinforcement Learning—0
SCC: an efficient deep reinforcement learning agent mastering the game of StarCraft II—0
Unsupervised deep clustering and reinforcement learning can accurately segment MRI brain tumors with very small training sets—0
Rethink AI-based Power Grid Control: Diving Into Algorithm Design—0
Deep Stock Trading: A Hierarchical Reinforcement Learning Framework for Portfolio Optimization and Order Execution—0
Augmenting Policy Learning with Routines Discovered from a Single DemonstrationCode1
Intelligent Reflecting Surface Assisted Anti-Jamming Communications Based on Reinforcement Learning—0
Intelligent Resource Allocation in Dense LoRa Networks using Deep Reinforcement Learning—0
A Dynamic Penalty Function Approach for Constraints-Handling in Reinforcement Learning—0
Scalable Deep Reinforcement Learning for Routing and Spectrum Access in Physical Layer—0
Self-Imitation Advantage Learning—0
QVMix and QVMix-Max: Extending the Deep Quality-Value Family of Algorithms to Cooperative Multi-Agent Reinforcement LearningCode0
myGym: Modular Toolkit for Visuomotor Robotic Tasks—0
Explicitly Encouraging Low Fractional Dimensional Trajectories Via Reinforcement LearningCode0
Difference Rewards Policy Gradients—0
Offline Reinforcement Learning from Images with Latent Space ModelsCode1
Mobile Robot Planner with Low-cost Cameras Using Deep Reinforcement Learning—0
Reinforcement Learning-based Product Delivery Frequency Control—0
Quantum reinforcement learning in continuous action space—0
Minimax Strikes Back—0
Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing ProblemsCode1
Model-Based Actor-Critic with Chance Constraint for Stochastic System—0
Generalize a Small Pre-trained Model to Arbitrarily Large TSP InstancesCode1
Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular NetworksCode1
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy ManagementCode1
Hierarchical principles of embodied reinforcement learning: A review—0
Exact Reduction of Huge Action Spaces in General Reinforcement Learning—0
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
Exploring Fluent Query Reformulations with Text-to-Text Transformers and Reinforcement Learning—0
Reinforcement Learning for Test Case Prioritization—0
Improving the Efficient Neural Architecture Search via Rewarding ModificationsCode0
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning—0
High-Throughput Synchronous Deep RLCode1
Interactive Question Clarification in Dialogue via Reinforcement Learning—0
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement LearningCode1
Autotelic Agents with Intrinsically Motivated Goal-Conditioned Reinforcement Learning: a Short Survey—0
MAGNet: Multi-agent Graph Network for Deep Multi-agent Reinforcement Learning—0
ViNG: Learning Open-World Navigation with Visual Goals—0
Model-free and Bayesian Ensembling Model-based Deep Reinforcement Learning for Particle Accelerator Control Demonstrated on the FERMI FELCode0
Towards Optimal District Heating Temperature Control in China with Deep Reinforcement Learning—0
CARLA Real Traffic Scenarios -- novel training ground and benchmark for autonomous driving—0
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph MatchingCode2
Learning to Run with Potential-Based Reward Shaping and Demonstrations from Video Data—0
A comparative evaluation of machine learning methods for robot navigation through human crowds—0
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation—0
Learning Accurate Long-term Dynamics for Model-based Reinforcement LearningCode2
Sample-Efficient Reinforcement Learning via Counterfactual-Based Data Augmentation—0
Grounding Artificial Intelligence in the Origins of Human Behavior—0
Gegelati: Lightweight Artificial Intelligence through Generic and Evolvable Tangled Program Graphs—0
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Benchmark Results

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