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 7601–7625 of 15113 papers

TitleStatusHype
Generating Interpretable Fuzzy Controllers using Particle Swarm Optimization and Genetic Programming—0
Generating Paraphrases with Lean Vocabulary—0
Improving Factual Consistency Between a Response and Persona Facts—0
Generating Rescheduling Knowledge using Reinforcement Learning in a Cognitive Architecture—0
Generating Socially Acceptable Perturbations for Efficient Evaluation of Autonomous Vehicles—0
Generating stable molecules using imitation and reinforcement learning—0
Generating Student Feedback from Time-Series Data Using Reinforcement Learning—0
Generating Text with Deep Reinforcement Learning—0
Generation of Policy-Level Explanations for Reinforcement Learning—0
Generative Adversarial Exploration for Reinforcement Learning—0
Generative Adversarial Imagination for Sample Efficient Deep Reinforcement Learning—0
Generative Adversarial Imitation Learning with Neural Networks: Global Optimality and Convergence Rate—0
Generative Adversarial Imitation Learning with Neural Network Parameterization: Global Optimality and Convergence Rate—0
Generative Adversarial Imitation Learning for End-to-End Autonomous Driving on Urban Environments—0
Generative Adversarial Reward Learning for Generalized Behavior Tendency Inference—0
Generative Adversarial Self-Imitation Learning—0
Generative Adversarial Simulator—0
Generative Design by Reinforcement Learning: Enhancing the Diversity of Topology Optimization Designs—0
Generative Exploration and Exploitation—0
Generative Inverse Deep Reinforcement Learning for Online Recommendation—0
Generative Job Recommendations with Large Language Model—0
Generative Memory for Lifelong Reinforcement Learning—0
Generative methods for sampling transition paths in molecular dynamics—0
Generative Multi-Agent Q-Learning for Policy Optimization: Decentralized Wireless Networks—0
Generative Slate Recommendation with Reinforcement Learning—0
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Benchmark Results

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