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 7451–7475 of 15113 papers

TitleStatusHype
Coordinated Random Access for Industrial IoT With Correlated Traffic By Reinforcement-Learning—0
Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach—0
Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning—0
Soft Actor-Critic With Integer Actions—0
RAPID-RL: A Reconfigurable Architecture with Preemptive-Exits for Efficient Deep-Reinforcement Learning—0
Reinforcement Learning on Encrypted Data—0
Enabling risk-aware Reinforcement Learning for medical interventions through uncertainty decomposition—0
Conservative Data Sharing for Multi-Task Offline Reinforcement Learning—0
Learning from Peers: Deep Transfer Reinforcement Learning for Joint Radio and Cache Resource Allocation in 5G RAN Slicing—0
Comparison and Unification of Three Regularization Methods in Batch Reinforcement Learning—0
Estimation of Warfarin Dosage with Reinforcement LearningCode0
Short Quantum Circuits in Reinforcement Learning Policies for the Vehicle Routing Problem—0
What Does The User Want? Information Gain for Hierarchical Dialogue Policy Optimisation—0
Optimal Cycling of a Heterogenous Battery Bank via Reinforcement Learning—0
Balancing detectability and performance of attacks on the control channel of Markov Decision ProcessesCode0
Back to Basics: Deep Reinforcement Learning in Traffic Signal ControlCode0
DCUR: Data Curriculum for Teaching via Samples with Reinforcement LearningCode0
Convergence of a Human-in-the-Loop Policy-Gradient Algorithm With Eligibility Trace Under Reward, Policy, and Advantage Feedback—0
Automatically Exposing Problems with Neural Dialog ModelsCode0
WaveCorr: Correlation-savvy Deep Reinforcement Learning for Portfolio ManagementCode0
ROMAX: Certifiably Robust Deep Multiagent Reinforcement Learning via Convex Relaxation—0
Reinforcement Learning with Evolutionary Trajectory Generator: A General Approach for Quadrupedal LocomotionCode1
Towards optimized actions in critical situations of soccer games with deep reinforcement learningCode0
Exploration in Deep Reinforcement Learning: From Single-Agent to Multiagent Domain—0
Continuous Homeostatic Reinforcement Learning for Self-Regulated Autonomous Agents—0
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Benchmark Results

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