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 11201–11250 of 15113 papers

TitleStatusHype
End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning—0
PCGRL: Procedural Content Generation via Reinforcement LearningCode1
Multi-objective Neural Architecture Search via Non-stationary Policy Gradient—0
Facial Feedback for Reinforcement Learning: A Case Study and Offline Analysis Using the TAMER Framework—0
Interpretable End-to-end Urban Autonomous Driving with Latent Deep Reinforcement LearningCode1
Graph Constrained Reinforcement Learning for Natural Language Action SpacesCode1
Reducing Non-Normative Text Generation from Language Models—0
GLIB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal BabblingCode0
Reinforcement Learning Based Vehicle-cell Association Algorithm for Highly Mobile Millimeter Wave Communication—0
On Simple Reactive Neural Networks for Behaviour-Based Reinforcement LearningCode1
On Solving Cooperative MARL Problems with a Few Good Experiences—0
Local Policy Optimization for Trajectory-Centric Reinforcement Learning—0
Emergence of Pragmatics from Referential Game between Theory of Mind AgentsCode0
Cooperative Highway Work Zone Merge Control based on Reinforcement Learning in A Connected and Automated Environment—0
Improving Interaction Quality Estimation with BiLSTMs and the Impact on Dialogue Policy Learning—0
Intelligent Bandwidth Allocation for Latency Management in NG-EPON using Reinforcement Learning Methods—0
Unsupervisedly Learned Representations: Should the Quest be Over?—0
Lyceum: An efficient and scalable ecosystem for robot learning—0
SARL*: Deep Reinforcement Learning based Human-Aware Navigation for Mobile Robot in Indoor EnvironmentsCode1
Reinforcement Learning with Probabilistically Complete Exploration—0
Nested-Wasserstein Self-Imitation Learning for Sequence Generation—0
Memristor Hardware-Friendly Reinforcement Learning—0
FRESH: Interactive Reward Shaping in High-Dimensional State Spaces using Human Feedback—0
A Survey of Reinforcement Learning Techniques: Strategies, Recent Development, and Future Directions—0
Discriminator Soft Actor Critic without Extrinsic RewardsCode1
Gradient Surgery for Multi-Task LearningCode1
Learning Options from Demonstration using Skill Segmentation—0
cube2net: Efficient Query-Specific Network Construction with Data Cube Organization—0
BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture—0
Multi-agent Motion Planning for Dense and Dynamic Environments via Deep Reinforcement Learning—0
Tree-Structured Policy based Progressive Reinforcement Learning for Temporally Language Grounding in VideoCode1
Algorithms in Multi-Agent Systems: A Holistic Perspective from Reinforcement Learning and Game Theory—0
Reward Shaping for Reinforcement Learning with Omega-Regular Objectives—0
MIME: Mutual Information Minimisation Exploration—0
Model-based Multi-Agent Reinforcement Learning with Cooperative Prioritized Sweeping—0
SEERL: Sample Efficient Ensemble Reinforcement Learning—0
Robotic Grasp Manipulation Using Evolutionary Computing and Deep Reinforcement Learning—0
Lipschitz Lifelong Reinforcement LearningCode1
Continuous-action Reinforcement Learning for Playing Racing Games: Comparing SPG to PPOCode0
PoPS: Policy Pruning and Shrinking for Deep Reinforcement LearningCode1
Multi-Robot Formation Control Using Reinforcement Learning—0
Statistical Inference of the Value Function for Reinforcement Learning in Infinite Horizon SettingsCode0
POPCORN: Partially Observed Prediction COnstrained ReiNforcement LearningCode1
GridMask Data AugmentationCode1
Exploiting Language Instructions for Interpretable and Compositional Reinforcement Learning—0
Learning to Locomote with Deep Neural-Network and CPG-based Control in a Soft Snake Robot—0
Weakly Supervised Video Summarization by Hierarchical Reinforcement Learning—0
Deep Reinforcement Learning for Complex Manipulation Tasks with Sparse Feedback—0
Sparse Black-box Video Attack with Reinforcement LearningCode0
Reward Engineering for Object Pick and Place TrainingCode0
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Benchmark Results

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