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 6101–6125 of 15113 papers

TitleStatusHype
Solving the swing-up and balance task for the Acrobot and Pendubot with SAC—0
Solving the vehicle routing problem with deep reinforcement learning—0
Some Supervision Required: Incorporating Oracle Policies in Reinforcement Learning via Epistemic Uncertainty Metrics—0
SoNIC: Safe Social Navigation with Adaptive Conformal Inference and Constrained Reinforcement Learning—0
SortingEnv: An Extendable RL-Environment for an Industrial Sorting Process—0
Source-Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language—0
Source Critical Reinforcement Learning for Transferring Spoken Language Understanding to a New Language—0
So You Think You Can Scale Up Autonomous Robot Data Collection?—0
Spacecraft Autonomous Decision-Planning for Collision Avoidance: a Reinforcement Learning Approach—0
Space Navigator: a Tool for the Optimization of Collision Avoidance Maneuvers—0
Space Processor Computation Time Analysis for Reinforcement Learning and Run Time Assurance Control Policies—0
Sparse Adversarial Attack in Multi-agent Reinforcement Learning—0
Sparse Attention Guided Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning—0
Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Models—0
Goal-constrained Sparse Reinforcement Learning for End-to-End Driving—0
Sparse Feature Selection Makes Batch Reinforcement Learning More Sample Efficient—0
Sparse Markov Decision Processes with Causal Sparse Tsallis Entropy Regularization for Reinforcement Learning—0
Sparse Meta Networks for Sequential Adaptation and its Application to Adaptive Language Modelling—0
Sparse Multi-Task Reinforcement Learning—0
Sparse Skill Coding: Learning Behavioral Hierarchies with Sparse Codes—0
Sparsity-Aware Intelligent Massive Random Access Control in Open RAN: A Reinforcement Learning Based Approach—0
Sparsity-based Safety Conservatism for Constrained Offline Reinforcement Learning—0
Reinforcement Learning With Sparse-Executing Actions via Sparsity Regularization—0
Spatial Assembly: Generative Architecture With Reinforcement Learning, Self Play and Tree Search—0
Spatial-aware decision-making with ring attractors in reinforcement learning systems—0
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Benchmark Results

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