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 10126–10150 of 15113 papers

TitleStatusHype
Double Meta-Learning for Data Efficient Policy Optimization in Non-Stationary Environments—0
Double Q(σ) and Q(σ, λ): Unifying Reinforcement Learning Control Algorithms—0
Double Q-learning—0
Double Reinforcement Learning for Efficient and Robust Off-Policy Evaluation—0
Doubly Robust Off-Policy Actor-Critic Algorithms for Reinforcement Learning—0
Doubly Robust Off-policy Value Evaluation for Reinforcement Learning—0
DPO: A Differential and Pointwise Control Approach to Reinforcement Learning—0
DQLAP: Deep Q-Learning Recommender Algorithm with Update Policy for a Real Steam Turbine System—0
DQNAS: Neural Architecture Search using Reinforcement Learning—0
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
Show:102550
← PrevPage 406 of 605Next →

Benchmark Results

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