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 11126–11150 of 15113 papers

TitleStatusHype
Truncating Trajectories in Monte Carlo Policy Evaluation: an Adaptive Approach—0
Trust-based Consensus in Multi-Agent Reinforcement Learning Systems—0
Trust-PCL: An Off-Policy Trust Region Method for Continuous Control—0
Trust the Model When It Is Confident: Masked Model-based Actor-Critic—0
Trustworthy Federated Learning via Blockchain—0
Trustworthy Reinforcement Learning Against Intrinsic Vulnerabilities: Robustness, Safety, and Generalizability—0
Tsallis Reinforcement Learning: A Unified Framework for Maximum Entropy Reinforcement Learning—0
t-Soft Update of Target Network for Deep Reinforcement Learning—0
Tuning computer vision models with task rewards—0
Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL—0
Tuning Path Tracking Controllers for Autonomous Cars Using Reinforcement Learning—0
Turbulence control in plane Couette flow using low-dimensional neural ODE-based models and deep reinforcement learning—0
Turning Mathematics Problems into Games: Reinforcement Learning and Gröbner bases together solve Integer Feasibility Problems—0
Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning—0
Tutorial on Course-of-Action (COA) Attack Search Methods in Computer Networks—0
Tutoring Reinforcement Learning via Feedback Control—0
TW-CRL: Time-Weighted Contrastive Reward Learning for Efficient Inverse Reinforcement Learning—0
Twisting Lids Off with Two Hands—0
Two Approaches to Building Collaborative, Task-Oriented Dialog Agents through Self-Play—0
Two Can Play That Game: An Adversarial Evaluation of a Cyber-alert Inspection System—0
Two-dimensional Anti-jamming Mobile Communication Based on Reinforcement Learning—0
Two geometric input transformation methods for fast online reinforcement learning with neural nets—0
Two-Hop Age of Information Scheduling for Multi-UAV Assisted Mobile Edge Computing: FRL vs MADDPG—0
Two-stage Deep Reinforcement Learning for Inverter-based Volt-VAR Control in Active Distribution Networks—0
Efficiently Training Deep-Learning Parametric Policies using Lagrangian Duality—0
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Benchmark Results

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