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 8001–8025 of 15113 papers

TitleStatusHype
Multi-subgoal Robot Navigation in Crowds with History Information and Interactions—0
Multi-Target Landmark Detection with Incomplete Images via Reinforcement Learning and Shape Prior—0
Multi-Task Federated Reinforcement Learning with Adversaries—0
Multi-Task Fusion via Reinforcement Learning for Long-Term User Satisfaction in Recommender Systems—0
Multi-task Learning for Continuous Control—0
Multi-task learning with deep model based reinforcement learning—0
Multi-Task Lifelong Reinforcement Learning for Wireless Sensor Networks—0
Multi-Task Policy Search—0
Multi-Task Reinforcement Learning based Mobile Manipulation Control for Dynamic Object Tracking and Grasping—0
Multi-Task Reinforcement Learning for Quadrotors—0
Multi-task Reinforcement Learning with a Planning Quasi-Metric—0
Multi-task Safe Reinforcement Learning for Navigating Intersections in Dense Traffic—0
Multi-Timescale, Gradient Descent, Temporal Difference Learning with Linear Options—0
Multi-timescale Nexting in a Reinforcement Learning Robot—0
Multi-timestep models for Model-based Reinforcement Learning—0
Multi-trainer Interactive Reinforcement Learning System—0
Multi-UAV Conflict Resolution with Graph Convolutional Reinforcement Learning—0
Multi-UAV Mobile Edge Computing and Path Planning Platform based on Reinforcement Learning—0
Multi-User Reinforcement Learning with Low Rank Rewards—0
Multi-user Resource Control with Deep Reinforcement Learning in IoT Edge Computing—0
Multi-Vehicle Mixed-Reality Reinforcement Learning for Autonomous Multi-Lane Driving—0
Multi-Vehicle Routing Problems with Soft Time Windows: A Multi-Agent Reinforcement Learning Approach—0
Multi-View Dreaming: Multi-View World Model with Contrastive Learning—0
Multi-zone HVAC Control with Model-Based Deep Reinforcement Learning—0
Mungojerrie: Reinforcement Learning of Linear-Time Objectives—0
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Benchmark Results

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