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 6651–6675 of 15113 papers

TitleStatusHype
DQN-based Beamforming for Uplink mmWave Cellular-Connected UAVs—0
DQN with model-based exploration: efficient learning on environments with sparse rewards—0
DR2L: Surfacing Corner Cases to Robustify Autonomous Driving via Domain Randomization Reinforcement Learning—0
DR3: Value-Based Deep Reinforcement Learning Requires Explicit Regularization—0
DRAS-CQSim: A Reinforcement Learning based Framework for HPC Cluster Scheduling—0
Drawing Inductor Layout with a Reinforcement Learning Agent: Method and Application for VCO Inductors—0
DRDT3: Diffusion-Refined Decision Test-Time Training Model—0
DREAM: Adaptive Reinforcement Learning based on Attention Mechanism for Temporal Knowledge Graph Reasoning—0
DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics—0
DreamerV3 for Traffic Signal Control: Hyperparameter Tuning and Performance—0
Dreaming machine learning: Lipschitz extensions for reinforcement learning on financial markets—0
Dreaming: Model-based Reinforcement Learning by Latent Imagination without Reconstruction—0
DreamingV2: Reinforcement Learning with Discrete World Models without Reconstruction—0
DRIFT: Deep Reinforcement Learning for Functional Software Testing—0
DRILL-- Deep Reinforcement Learning for Refinement Operators in ALC—0
DriveMind: A Dual-VLM based Reinforcement Learning Framework for Autonomous Driving—0
Driver Assistance Eco-driving and Transmission Control with Deep Reinforcement Learning—0
DriverGym: Democratising Reinforcement Learning for Autonomous Driving—0
Driver Modeling through Deep Reinforcement Learning and Behavioral Game Theory—0
Driving Decision and Control for Autonomous Lane Change based on Deep Reinforcement Learning—0
Driving in Real Life with Inverse Reinforcement Learning—0
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning—0
Driving Tasks Transfer in Deep Reinforcement Learning for Decision-making of Autonomous Vehicles—0
Driving with Style: Inverse Reinforcement Learning in General-Purpose Planning for Automated Driving—0
DRL-Based QoS-Aware Resource Allocation Scheme for Coexistence of Licensed and Unlicensed Users in LTE and Beyond—0
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Benchmark Results

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