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 6151–6175 of 15113 papers

TitleStatusHype
Argumentative Reward Learning: Reasoning About Human Preferences—0
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
Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265—0
Reinforcement Learning with Non-Exponential Discounting—0
Safe Reinforcement Learning of Dynamic High-Dimensional Robotic Tasks: Navigation, Manipulation, Interaction—0
Reinforcement Learning for Cognitive Delay/Disruption Tolerant Network Node Management in an LEO-based Satellite Constellation—0
Paused Agent Replay Refresh—0
Overcoming Referential Ambiguity in Language-Guided Goal-Conditioned Reinforcement Learning—0
Understanding Hindsight Goal Relabeling from a Divergence Minimization Perspective—0
DEFT: Diverse Ensembles for Fast Transfer in Reinforcement Learning—0
Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis—0
Improving Document Image Understanding with Reinforcement Finetuning—0
Delayed Geometric Discounts: An Alternative Criterion for 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
Opportunities and Challenges from Using Animal Videos in Reinforcement Learning for Navigation—0
Reward Learning using Structural Motifs in Inverse Reinforcement Learning—0
Explainable Reinforcement Learning via Model TransformsCode0
Fast Lifelong Adaptive Inverse Reinforcement Learning from Demonstrations—0
Unified Algorithms for RL with Decision-Estimation Coefficients: PAC, Reward-Free, Preference-Based Learning, and Beyond—0
SAFER: Safe Collision Avoidance using Focused and Efficient Trajectory Search with Reinforcement Learning—0
Quantification before Selection: Active Dynamics Preference for Robust Reinforcement Learning—0
Minimizing Human Assistance: Augmenting a Single Demonstration for Deep Reinforcement Learning—0
Reinforcement Learning in Computing and Network Convergence Orchestration—0
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Benchmark Results

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