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 3651–3675 of 15113 papers

TitleStatusHype
Value of Information and Reward Specification in Active Inference and POMDPs—0
Introduction to Reinforcement Learning—0
GFlowNet Training by Policy GradientsCode0
Online Optimization of Curriculum Learning Schedules using Evolutionary Optimization—0
The Bandit Whisperer: Communication Learning for Restless Bandits—0
Optimizing Portfolio with Two-Sided Transactions and Lending: A Reinforcement Learning Framework—0
Deep Reinforcement Learning for the Design of Metamaterial Mechanisms with Functional Compliance Control—0
Hybrid Reinforcement Learning Breaks Sample Size Barriers in Linear MDPs—0
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes—0
PLANRL: A Motion Planning and Imitation Learning Framework to Bootstrap Reinforcement Learning—0
Learning Rate-Free Reinforcement Learning: A Case for Model Selection with Non-Stationary ObjectivesCode0
Model-free optimal controller for discrete-time Markovian jump linear systems: A Q-learning approach—0
CADRL: Category-aware Dual-agent Reinforcement Learning for Explainable Recommendations over Knowledge Graphs—0
Integrating Controllable Motion Skills from Demonstrations—0
Full error analysis of policy gradient learning algorithms for exploratory linear quadratic mean-field control problem in continuous time with common noise—0
Active Sensing of Knee Osteoarthritis Progression with Reinforcement Learning—0
Reinforcement Learning for an Efficient and Effective Malware Investigation during Cyber Incident Response—0
Coordinating Planning and Tracking in Layered Control Policies via Actor-Critic LearningCode0
Multi-Objective Deep Reinforcement Learning for Optimisation in Autonomous Systems—0
TCR-GPT: Integrating Autoregressive Model and Reinforcement Learning for T-Cell Receptor Repertoires Generation—0
A Survey on Self-play Methods in Reinforcement Learning—0
Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment—0
On the Perturbed States for Transformed Input-robust Reinforcement LearningCode0
Image-Based Deep Reinforcement Learning with Intrinsically Motivated Stimuli: On the Execution of Complex Robotic Tasks—0
Multi-agent Assessment with QoS Enhancement for HD Map Updates in a Vehicular Network—0
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Benchmark Results

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