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 8701–8725 of 15113 papers

TitleStatusHype
Quality Signals in Generated Stories—0
Quantification before Selection: Active Dynamics Preference for Robust Reinforcement Learning—0
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning—0
Quantifying Multimodality in World Models—0
Quantifying the Effect of Feedback Frequency in Interactive Reinforcement Learning for Robotic Tasks—0
Quantifying the Impact of Non-Stationarity in Reinforcement Learning-Based Traffic Signal Control—0
Quantile-Based Policy Optimization for Reinforcement Learning—0
Quantile QT-Opt for Risk-Aware Vision-Based Robotic Grasping—0
Quantile Reinforcement Learning—0
Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation—0
Quantitative Day Trading from Natural Language using Reinforcement Learning—0
Quantitative Resilience Modeling for Autonomous Cyber Defense—0
Quantitative Trading using Deep Q Learning—0
Quantity vs. Quality: On Hyperparameter Optimization for Deep Reinforcement Learning—0
Quantum algorithms applied to satellite mission planning for Earth observation—0
Quantum Architecture Search via Continual Reinforcement Learning—0
Quantum Compiling with Reinforcement Learning on a Superconducting Processor—0
Quantum Computing Provides Exponential Regret Improvement in Episodic Reinforcement Learning—0
Quantum Control based on Deep Reinforcement Learning—0
Quantum deep recurrent reinforcement learning—0
Quantum-enhanced machine learning—0
Quantum-Enhanced Reinforcement Learning for Power Grid Security Assessment—0
Quantum framework for Reinforcement Learning: Integrating Markov decision process, quantum arithmetic, and trajectory search—0
Quantum Logic Gate Synthesis as a Markov Decision Process—0
Quantum machine learning with glow for episodic tasks and decision games—0
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Benchmark Results

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