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 10526–10550 of 15113 papers

TitleStatusHype
Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity—0
Proximal Gradient Temporal Difference Learning: Stable Reinforcement Learning with Polynomial Sample ComplexityCode1
Stable and Efficient Policy Evaluation—0
State Action Separable Reinforcement Learning—0
AutoHAS: Efficient Hyperparameter and Architecture Search—0
Deployment-Efficient Reinforcement Learning via Model-Based Offline OptimizationCode1
Curiosity Killed or Incapacitated the Cat and the Asymptotically Optimal AgentCode0
Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval—0
Visual Transfer for Reinforcement Learning via Wasserstein Domain ConfusionCode0
Meta-Model-Based Meta-Policy Optimization—0
Single-step deep reinforcement learning for open-loop control of laminar and turbulent flowsCode1
Solving Hard AI Planning Instances Using Curriculum-Driven Deep Reinforcement LearningCode1
Refined Continuous Control of DDPG Actors via Parametrised Activation—0
Constrained Reinforcement Learning for Dynamic Optimization under Uncertainty—0
A Novel Update Mechanism for Q-Networks Based On Extreme Learning MachinesCode0
Causality and Batch Reinforcement Learning: Complementary Approaches To Planning In Unknown Domains—0
Interferobot: aligning an optical interferometer by a reinforcement learning agentCode1
Learning to Scan: A Deep Reinforcement Learning Approach for Personalized Scanning in CT Imaging—0
The Value-Improvement Path: Towards Better Representations for Reinforcement Learning—0
Temporally-Extended ε-Greedy ExplorationCode0
Jointly Learning Environments and Control Policies with Projected Stochastic Gradient AscentCode0
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient ExplorationCode0
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
Active Vision for Early Recognition of Human Actions—0
Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning—0
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Benchmark Results

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