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 6351–6375 of 15113 papers

TitleStatusHype
Depth-CUPRL: Depth-Imaged Contrastive Unsupervised Prioritized Representations in Reinforcement Learning for Mapless Navigation of Unmanned Aerial Vehicles—0
Depthwise Convolution for Multi-Agent Communication with Enhanced Mean-Field Approximation—0
Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity—0
Derivative-Free Reinforcement Learning: A Review—0
Description Based Text Classification with Reinforcement Learning—0
Design and Comparison of Reward Functions in Reinforcement Learning for Energy Management of Sensor Nodes—0
Design and Development of Spoken Dialogue System in Indic Languages—0
Design and Experimental Test of Datatic Approximate Optimal Filter in Nonlinear Dynamic Systems—0
Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning—0
Design for a Darwinian Brain: Part 2. Cognitive Architecture—0
Designing a Multi-Objective Reward Function for Creating Teams of Robotic Bodyguards Using Deep Reinforcement Learning—0
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning—0
Designing Biological Sequences via Meta-Reinforcement Learning and Bayesian Optimization—0
Designing Composites with Target Effective Young's Modulus using Reinforcement Learning—0
Designing Deep Reinforcement Learning for Human Parameter Exploration—0
Designing high-fidelity multi-qubit gates for semiconductor quantum dots through deep reinforcement learning—0
Designing Interpretable Approximations to Deep Reinforcement Learning—0
Designing Optimal Dynamic Treatment Regimes: A Causal Reinforcement Learning Approach—0
Designing realistic RL environment for power systems—0
Designing Rewards for Fast Learning—0
Design of Artificial Intelligence Agents for Games using Deep Reinforcement Learning—0
Design of experiments for the calibration of history-dependent models via deep reinforcement learning and an enhanced Kalman filter—0
Design of Interacting Particle Systems for Fast Linear Quadratic RL—0
Design of Restricted Normalizing Flow towards Arbitrary Stochastic Policy with Computational Efficiency—0
Design Principles of the Hippocampal Cognitive Map—0
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Benchmark Results

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