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

Showing 871880 of 15113 papers

TitleStatusHype
Targeted Adversarial Attacks on Deep Reinforcement Learning Policies via Model CheckingCode1
Compiler Optimization for Quantum Computing Using Reinforcement LearningCode1
PrefRec: Recommender Systems with Human Preferences for Reinforcing Long-term User EngagementCode1
Physics-Informed Model-Based Reinforcement LearningCode1
RLogist: Fast Observation Strategy on Whole-slide Images with Deep Reinforcement LearningCode1
MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid DynamicsCode1
Karolos: An Open-Source Reinforcement Learning Framework for Robot-Task EnvironmentsCode1
ConvLab-3: A Flexible Dialogue System Toolkit Based on a Unified Data FormatCode1
One Risk to Rule Them All: A Risk-Sensitive Perspective on Model-Based Offline Reinforcement LearningCode1
Real-time Bidding Strategy in Display Advertising: An Empirical AnalysisCode1
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Benchmark Results

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