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 11076–11100 of 15113 papers

TitleStatusHype
General Method for Solving Four Types of SAT Problems—0
General sum stochastic games with networked information flows—0
Generate and Revise: Reinforcement Learning in Neural Poetry—0
Generating and Evolving Reward Functions for Highway Driving with Large Language Models—0
Generating Black-Box Adversarial Examples for Text Classifiers Using a Deep Reinforced Model—0
Generating Critical Scenarios for Testing Automated Driving Systems—0
Generating Explanations from Deep Reinforcement Learning Using Episodic Memory—0
Generating Formality-Tuned Summaries Using Input-Dependent Rewards—0
Generating GPU Compiler Heuristics using Reinforcement Learning—0
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
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Benchmark Results

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