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 4776–4800 of 15113 papers

TitleStatusHype
Guiding Safe Exploration with Weakest Preconditions—0
Exploiting Transformer in Sparse Reward Reinforcement Learning for Interpretable Temporal Logic Motion PlanningCode1
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter—0
DCE: Offline Reinforcement Learning With Double Conservative Estimates—0
Reinforcement Learning for Cognitive Delay/Disruption Tolerant Network Node Management in an LEO-based Satellite Constellation—0
Reinforcement Learning with Non-Exponential Discounting—0
Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265—0
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction—0
Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis—0
End-to-End Affordance Learning for Robotic ManipulationCode1
Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward EnvironmentsCode1
Improving Document Image Understanding with Reinforcement Finetuning—0
Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective—0
Training Efficient Controllers via Analytic Policy GradientCode1
Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning—0
Paused Agent Replay Refresh—0
Delayed Geometric Discounts: An Alternative Criterion for Reinforcement Learning—0
DEFT: Diverse Ensembles for Fast Transfer in Reinforcement Learning—0
Deep Reinforcement Learning for Adaptive Mesh Refinement—0
Unsupervised Reward Shaping for a Robotic Sequential Picking Task from Visual Observations in a Logistics ScenarioCode0
Reward Learning using Structural Motifs in Inverse Reinforcement Learning—0
Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation—0
Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations—0
Explainable Reinforcement Learning via Model TransformsCode0
Mastering the Unsupervised Reinforcement Learning Benchmark from PixelsCode1
Show:102550
← PrevPage 192 of 605Next →

Benchmark Results

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