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 8226–8250 of 15113 papers

TitleStatusHype
Preference Adaptive and Sequential Text-to-Image Generation—0
Personalized Cancer Chemotherapy Schedule: a numerical comparison of performance and robustness in model-based and model-free scheduling methodologies—0
Personalized Dynamic Pricing Policy for Electric Vehicles: Reinforcement learning approach—0
Personalized Education at Scale—0
Personalized Exposure Control Using Adaptive Metering and Reinforcement Learning—0
Personalized Federated Hypernetworks for Privacy Preservation in Multi-Task Reinforcement Learning—0
Personalized HeartSteps: A Reinforcement Learning Algorithm for Optimizing Physical Activity—0
Personalized Lane Change Decision Algorithm Using Deep Reinforcement Learning Approach—0
Personalized Medical Treatments Using Novel Reinforcement Learning Algorithms—0
Personalizing a Dialogue System with Transfer Reinforcement Learning—0
Perspectives on the Social Impacts of Reinforcement Learning with Human Feedback—0
Perspective Taking in Deep Reinforcement Learning Agents—0
Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning—0
Persuading to Prepare for Quitting Smoking with a Virtual Coach: Using States and User Characteristics to Predict Behavior—0
Perturbational Complexity by Distribution Mismatch: A Systematic Analysis of Reinforcement Learning in Reproducing Kernel Hilbert Space—0
Perturbation-based exploration methods in deep reinforcement learning—0
Pessimism in the Face of Confounders: Provably Efficient Offline Reinforcement Learning in Partially Observable Markov Decision Processes—0
Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning—0
Pessimism meets VCG: Learning Dynamic Mechanism Design via Offline Reinforcement Learning—0
Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data—0
Pessimistic Model-based Offline Reinforcement Learning under Partial Coverage—0
Pessimistic Model Selection for Offline Deep Reinforcement Learning—0
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning—0
Pessimistic Q-Learning for Offline Reinforcement Learning: Towards Optimal Sample Complexity—0
Petri Net Machines for Human-Agent Interaction—0
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Benchmark Results

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