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 9826–9850 of 15113 papers

TitleStatusHype
Density-Based Bonuses on Learned Representations for Reward-Free Exploration in Deep Reinforcement Learning—0
Density Constrained Reinforcement Learning—0
Dependency-Aware Computation Offloading in Mobile Edge Computing: A Reinforcement Learning Approach—0
Dependency Parsing with Backtracking using Deep Reinforcement Learning—0
Depending on yourself when you should: Mentoring LLM with RL agents to become the master in cybersecurity games—0
Deploying Offline Reinforcement Learning with Human Feedback—0
Deploying Reinforcement Learning in Water Transport—0
Depth and nonlinearity induce implicit exploration for RL—0
Depth-Constrained ASV Navigation with Deep RL and Limited Sensing—0
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
Show:102550
← PrevPage 394 of 605Next →

Benchmark Results

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