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

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Showing 10901–10925 of 15113 papers

TitleStatusHype
Balancing Reinforcement Learning Training Experiences in Interactive Information Retrieval—0
AutoHAS: Efficient Hyperparameter and Architecture Search—0
State Action Separable Reinforcement Learning—0
Refined Continuous Control of DDPG Actors via Parametrised Activation—0
Visual Transfer for Reinforcement Learning via Wasserstein Domain ConfusionCode0
Meta-Model-Based Meta-Policy Optimization—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
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
Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient ExplorationCode0
Jointly Learning Environments and Control Policies with Projected Stochastic Gradient AscentCode0
Active Vision for Early Recognition of Human Actions—0
A novel approach for multi-agent cooperative pursuit to capture grouped evaders—0
Reinforcement learning and Bayesian data assimilation for model-informed precision dosing in oncology—0
Mitigating Bias in Face Recognition Using Skewness-Aware Reinforcement Learning—0
Temporal-Differential Learning in Continuous Environments—0
Robust Reinforcement Learning with Wasserstein Constraint—0
Model-Based Reinforcement Learning with Value-Targeted Regression—0
Variational Reward Estimator Bottleneck: Learning Robust Reward Estimator for Multi-Domain Task-Oriented Dialog—0
MM-KTD: Multiple Model Kalman Temporal Differences for Reinforcement LearningCode0
Reinforcement LearningCode0
AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments—0
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Benchmark Results

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