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 2626–2650 of 15113 papers

TitleStatusHype
Parameterized Projected Bellman OperatorCode0
BadRL: Sparse Targeted Backdoor Attack Against Reinforcement LearningCode0
Data-Driven Merton's Strategies via Policy Randomization—0
Stable Relay Learning Optimization Approach for Fast Power System Production Cost Minimization Simulation—0
CUDC: A Curiosity-Driven Unsupervised Data Collection Method with Adaptive Temporal Distances for Offline Reinforcement Learning—0
A Dual Curriculum Learning Framework for Multi-UAV Pursuit-Evasion in Diverse Environments—0
Neural Network Approximation for Pessimistic Offline Reinforcement Learning—0
Solving the swing-up and balance task for the Acrobot and Pendubot with SAC—0
Safeguarded Progress in Reinforcement Learning: Safe Bayesian Exploration for Control Policy Synthesis—0
Active search and coverage using point-cloud reinforcement learning—0
Challenges for Reinforcement Learning in Quantum Circuit DesignCode1
Learning to Act without ActionsCode1
CACTO-SL: Using Sobolev Learning to improve Continuous Actor-Critic with Trajectory OptimizationCode1
Improving Environment Robustness of Deep Reinforcement Learning Approaches for Autonomous Racing Using Bayesian Optimization-based Curriculum LearningCode0
Active Reinforcement Learning for Robust Building ControlCode1
Imitate the Good and Avoid the Bad: An Incremental Approach to Safe Reinforcement LearningCode0
Advancing RAN Slicing with Offline Reinforcement Learning—0
Online Restless Multi-Armed Bandits with Long-Term Fairness Constraints—0
Fractional Deep Reinforcement Learning for Age-Minimal Mobile Edge Computing—0
Assume-Guarantee Reinforcement Learning—0
Toward Computationally Efficient Inverse Reinforcement Learning via Reward Shaping—0
Towards Automatic Data Augmentation for Disordered Speech Recognition—0
iOn-Profiler: intelligent Online multi-objective VNF Profiling with Reinforcement Learning—0
ReCoRe: Regularized Contrastive Representation Learning of World Model—0
World Models via Policy-Guided Trajectory DiffusionCode1
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Benchmark Results

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