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 7226–7250 of 15113 papers

TitleStatusHype
Multi-Agent Broad Reinforcement Learning for Intelligent Traffic Light Control—0
Rényi State Entropy for Exploration Acceleration in Reinforcement Learning—0
Designing Heterogeneous GNNs with Desired Permutation Properties for Wireless Resource Allocation—0
A Complete Characterization of Linear Estimators for Offline Policy Evaluation—0
Graph-based Reinforcement Learning meets Mixed Integer Programs: An application to 3D robot assembly discovery—0
Distributed Control using Reinforcement Learning with Temporal-Logic-Based Reward Shaping—0
Knowledge Transfer in Deep Reinforcement Learning for Slice-Aware Mobility Robustness Optimization—0
Graph Neural Networks for Image Classification and Reinforcement Learning using Graph representations—0
Cascaded Gaps: Towards Gap-Dependent Regret for Risk-Sensitive Reinforcement Learning—0
A Survey on Reinforcement Learning Methods in Character Animation—0
Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets—0
Efficient Policy Generation in Multi-Agent Systems via Hypergraph Neural Network—0
Fast and Data Efficient Reinforcement Learning from Pixels via Non-Parametric Value Approximation—0
Scalable multi-agent reinforcement learning for distributed control of residential energy flexibility—0
Black-Box Safety Validation of Autonomous Systems: A Multi-Fidelity Reinforcement Learning Approach—0
Reinforcement Learning for Location-Aware Scheduling—0
On Credit Assignment in Hierarchical Reinforcement LearningCode0
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning—0
Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit—0
Watch from sky: machine-learning-based multi-UAV network for predictive police surveillance—0
Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment—0
Deep Reinforcement Learning based Model-free On-line Dynamic Multi-Microgrid Formation to Enhance Resilience—0
Leveraging Reward Gradients For Reinforcement Learning in Differentiable Physics Simulations—0
Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation—0
A Multi-Document Coverage Reward for RELAXed Multi-Document SummarizationCode0
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Benchmark Results

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