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 5201–5225 of 15113 papers

TitleStatusHype
Hindsight Learning for MDPs with Exogenous InputsCode0
Brick Tic-Tac-Toe: Exploring the Generalizability of AlphaZero to Novel Test EnvironmentsCode0
GriddlyJS: A Web IDE for Reinforcement Learning—0
Continual Meta-Reinforcement Learning for UAV-Aided Vehicular Wireless Networks—0
Towards Global Optimality in Cooperative MARL with the Transformation And Distillation Framework—0
Optimistic PAC Reinforcement Learning: the Instance-Dependent View—0
Learning Bellman Complete Representations for Offline Policy EvaluationCode0
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy OptimizationCode1
Temporal Disentanglement of Representations for Improved Generalisation in Reinforcement LearningCode1
Online Game Level Generation from MusicCode0
Reactive Exploration to Cope with Non-Stationarity in Lifelong Reinforcement LearningCode1
PAC Reinforcement Learning for Predictive State Representations—0
Grounding Aleatoric Uncertainty for Unsupervised Environment Design—0
Don't Start From Scratch: Leveraging Prior Data to Automate Robotic Reinforcement Learning—0
Learning Temporally Extended Skills in Continuous Domains as Symbolic Actions for Planning—0
Reinforcement Learningx2013Based Transient Response Shaping for Microgrids—0
State Dropout-Based Curriculum Reinforcement Learning for Self-Driving at Unsignalized Intersections—0
Deep Reinforcement Learning for Long-Term Voltage Stability Control—0
Ablation Study of How Run Time Assurance Impacts the Training and Performance of Reinforcement Learning Agents—0
High Performance Simulation for Scalable Multi-Agent Reinforcement Learning—0
Interaction Pattern Disentangling for Multi-Agent Reinforcement LearningCode1
CompoSuite: A Compositional Reinforcement Learning BenchmarkCode1
Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse ManagementCode1
Safe reinforcement learning for multi-energy management systems with known constraint functions—0
Reinforced Lin-Kernighan-Helsgaun Algorithms for the Traveling Salesman ProblemsCode1
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Benchmark Results

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