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 9626–9650 of 15113 papers

TitleStatusHype
Visual Navigation in Real-World Indoor Environments Using End-to-End Deep Reinforcement LearningCode1
Reinforcement learning using Deep Q Networks and Q learning accurately localizes brain tumors on MRI with very small training sets—0
Logistic Q-Learning—0
Safety Verification of Model Based Reinforcement Learning Controllers—0
PARENTing via Model-Agnostic Reinforcement Learning to Correct Pathological Behaviors in Data-to-Text GenerationCode1
On Information Asymmetry in Competitive Multi-Agent Reinforcement Learning: Convergence and Optimality—0
Improving Generalization in Reinforcement Learning with Mixture RegularizationCode1
Correlation-aware Cooperative Multigroup Broadcast 360° Video Delivery Network: A Hierarchical Deep Reinforcement Learning ApproachCode1
Multi-Radar Tracking Optimization for Collaborative Combat—0
Quality of service based radar resource management using deep reinforcement learning—0
Runtime Safety Assurance Using Reinforcement Learning—0
Negotiating Team Formation Using Deep Reinforcement Learning—0
Robust Constrained Reinforcement Learning for Continuous Control with Model Misspecification—0
Reinforcement Learning for Optimization of COVID-19 Mitigation policiesCode1
Iterative Amortized Policy OptimizationCode1
Integrating LEO Satellites and Multi-UAV Reinforcement Learning for Hybrid FSO/RF Non-Terrestrial Networks—0
Deep Reinforcement Learning in Lane Merge Coordination for Connected Vehicles—0
Language Inference with Multi-head Automata through Reinforcement Learning—0
A case for new neural networks smoothness constraints—0
Dream and Search to Control: Latent Space Planning for Continuous ControlCode1
Learning by Competition of Self-Interested Reinforcement Learning AgentsCode0
Imitation with Neural Density Models—0
Connections between Relational Event Model and Inverse Reinforcement Learning for Characterizing Group Interaction SequencesCode2
SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous DrivingCode2
Model-based Policy Optimization with Unsupervised Model AdaptationCode1
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Benchmark Results

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