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 5376–5400 of 15113 papers

TitleStatusHype
A Near-Optimal Algorithm for Safe Reinforcement Learning Under Instantaneous Hard Constraints—0
AISYN: AI-driven Reinforcement Learning-Based Logic Synthesis Framework—0
Near-Optimal Adversarial Reinforcement Learning with Switching Costs—0
Non-zero-sum Game Control for Multi-vehicle Driving via Reinforcement LearningCode0
Near-Minimax-Optimal Risk-Sensitive Reinforcement Learning with CVaR—0
Optimizing Audio Recommendations for the Long-Term: A Reinforcement Learning Perspective—0
Transfer learning for process design with reinforcement learning—0
Towards Skilled Population Curriculum for Multi-Agent Reinforcement Learning—0
Online Reinforcement Learning with Uncertain Episode Lengths—0
Ensemble Value Functions for Efficient Exploration in Multi-Agent Reinforcement Learning—0
Adaptive Aggregation for Safety-Critical Control—0
Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback—0
Arena-Web -- A Web-based Development and Benchmarking Platform for Autonomous Navigation Approaches—0
Holistic Deep-Reinforcement-Learning-based Training of Autonomous Navigation Systems—0
DITTO: Offline Imitation Learning with World Models—0
Intrinsic Rewards from Self-Organizing Feature Maps for Exploration in Reinforcement LearningCode0
A Strong Baseline for Batch Imitation Learning—0
State-wise Safe Reinforcement Learning: A Survey—0
RLTP: Reinforcement Learning to Pace for Delayed Impression Modeling in Preloaded Ads—0
Offline Learning in Markov Games with General Function Approximation—0
Offline Learning of Closed-Loop Deep Brain Stimulation Controllers for Parkinson Disease TreatmentCode0
Open Problems and Modern Solutions for Deep Reinforcement Learning—0
Model-free Quantum Gate Design and Calibration using Deep Reinforcement LearningCode0
Offline Minimax Soft-Q-learning Under Realizability and Partial Coverage—0
An Online Model-Following Projection Mechanism Using Reinforcement Learning—0
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Benchmark Results

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