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 5801–5825 of 15113 papers

TitleStatusHype
Scheduling Out-of-Coverage Vehicular Communications Using Reinforcement Learning—0
Scheduling the NASA Deep Space Network with Deep Reinforcement Learning—0
School of hard knocks: Curriculum analysis for Pommerman with a fixed computational budget—0
Scientific Discovery and the Cost of Measurement -- Balancing Information and Cost in Reinforcement Learning—0
Scientific multi-agent reinforcement learning for wall-models of turbulent flows—0
Scilab-RL: A software framework for efficient reinforcement learning and cognitive modeling research—0
Score-Aware Policy-Gradient Methods and Performance Guarantees using Local Lyapunov Conditions: Applications to Product-Form Stochastic Networks and Queueing Systems—0
Scores as Actions: a framework of fine-tuning diffusion models by continuous-time reinforcement learning—0
SDGym: Low-Code Reinforcement Learning Environments using System Dynamics Models—0
SDN Flow Entry Management Using Reinforcement Learning—0
SDRL: Interpretable and Data-efficient Deep Reinforcement Learning Leveraging Symbolic Planning—0
Search-Based Testing of Reinforcement Learning—0
Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs—0
Searching for High-Value Molecules Using Reinforcement Learning and Transformers—0
Searching Learning Strategy with Reinforcement Learning for 3D Medical Image Segmentation—0
Search on the Replay Buffer: Bridging Planning and Reinforcement Learning—0
Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits—0
SECRM-2D: RL-Based Efficient and Comfortable Route-Following Autonomous Driving with Analytic Safety Guarantees—0
Secure Computation Offloading in Blockchain based IoT Networks with Deep Reinforcement Learning—0
Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms—0
Security-Aware Virtual Network Embedding Algorithm based on Reinforcement Learning—0
SeedNet: Automatic Seed Generation With Deep Reinforcement Learning for Robust Interactive Segmentation—0
Seeing by haptic glance: reinforcement learning-based 3D object Recognition—0
Seeing-Eye Quadruped Navigation with Force Responsive Locomotion Control—0
Seeing is not Believing: Robust Reinforcement Learning against Spurious Correlation—0
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Benchmark Results

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