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 6051–6075 of 15113 papers

TitleStatusHype
Investigation of Factorized Optical Flows as Mid-Level Representations—0
Reinforced MOOCs Concept Recommendation in Heterogeneous Information Networks—0
Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control—0
Rényi State Entropy for Exploration Acceleration in Reinforcement Learning—0
A Complete Characterization of Linear Estimators for Offline Policy Evaluation—0
Curriculum-based Reinforcement Learning for Distribution System Critical Load RestorationCode1
Designing Heterogeneous GNNs with Desired Permutation Properties for Wireless Resource Allocation—0
Distributed Control using Reinforcement Learning with Temporal-Logic-Based Reward Shaping—0
Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery—0
Policy-Based Bayesian Experimental Design for Non-Differentiable Implicit Models—0
Robot Learning of Mobile Manipulation with Reachability Behavior Priors—0
A Survey on Reinforcement Learning Methods in Character Animation—0
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets—0
Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation—0
Deep Reinforcement Learning for Entity AlignmentCode1
Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations—0
Influencing Long-Term Behavior in Multiagent Reinforcement LearningCode1
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network—0
Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization—0
Cascaded Gaps: Towards Gap-Dependent Regret for Risk-Sensitive Reinforcement Learning—0
Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility—0
Reliably Re-Acting to Partner's Actions with the Social Intrinsic Motivation of Transfer EmpowermentCode1
Reinforcement Learning for Location-Aware Scheduling—0
On Credit Assignment in Hierarchical Reinforcement LearningCode0
Black-Box Safety Validation of Autonomous Systems: A Multi-Fidelity Reinforcement Learning Approach—0
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Benchmark Results

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