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 10426–10450 of 15113 papers

TitleStatusHype
End-to-End Policy Gradient Method for POMDPs and Explainable Agents—0
End-to-End Race Driving with Deep Reinforcement Learning—0
End-to-end Reinforcement Learning of Robotic Manipulation with Robust Keypoints Representation—0
End-to-End Vision-Based Adaptive Cruise Control (ACC) Using Deep Reinforcement Learning—0
Energy Aware Deep Reinforcement Learning Scheduling for Sensors Correlated in Time and Space—0
Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach—0
Energy-aware Scheduling of Jobs in Heterogeneous Cluster Systems Using Deep Reinforcement Learning—0
Energy Efficiency in Reinforcement Learning for Wireless Sensor Networks—0
Energy Efficiency Optimization for Subterranean LoRaWAN Using A Reinforcement Learning Approach: A Direct-to-Satellite Scenario—0
Energy-Efficient Design for a NOMA assisted STAR-RIS Network with Deep Reinforcement Learning—0
Energy Efficient Edge Computing: When Lyapunov Meets Distributed Reinforcement Learning—0
Energy Expenditure Estimation Through Daily Activity Recognition Using a Smart-phone—0
Energy Management Based on Multi-Agent Deep Reinforcement Learning for A Multi-Energy Industrial Park—0
Energy Optimization of Wind Turbines via a Neural Control Policy Based on Reinforcement Learning Markov Chain Monte Carlo Algorithm—0
Energy Saving in 6G O-RAN Using DQN-based xApp—0
Energy Storage Arbitrage in Real-Time Markets via Reinforcement Learning—0
Energy Storage Management via Deep Q-Networks—0
Energy-Weighted Flow Matching for Offline Reinforcement Learning—0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Enforcing constraints for time series prediction in supervised, unsupervised and reinforcement learning—0
Enforcing Hard Constraints with Soft Barriers: Safe Reinforcement Learning in Unknown Stochastic Environments—0
Enforcing KL Regularization in General Tsallis Entropy Reinforcement Learning via Advantage Learning—0
Enforcing the consensus between Trajectory Optimization and Policy Learning for precise robot control—0
Enhanced Adversarial Strategically-Timed Attacks against Deep Reinforcement Learning—0
Enhanced Audit Techniques Empowered by the Reinforcement Learning Pertaining to IFRS 16 Lease—0
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Benchmark Results

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