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 7201–7225 of 15113 papers

TitleStatusHype
VisualHints: A Visual-Lingual Environment for Multimodal Reinforcement Learning—0
Towards Learning to Imitate from a Single Video Demonstration—0
Visual Imitation with Reinforcement Learning using Recurrent Siamese Networks—0
Visualizing the Loss Landscape of Actor Critic Methods with Applications in Inventory Optimization—0
Visual-Policy Learning through Multi-Camera View to Single-Camera View Knowledge Distillation for Robot Manipulation Tasks—0
Visual processing in context of reinforcement learning—0
Visual Radial Basis Q-Network—0
Visual Rationalizations in Deep Reinforcement Learning for Atari Games—0
Visual search and recognition for robot task execution and monitoring—0
Visual Semantic Planning using Deep Successor Representations—0
Visual Sensor Network Reconfiguration with Deep Reinforcement Learning—0
Software Simulation and Visualization of Quantum Multi-Drone Reinforcement Learning—0
Visual-Tactile Multimodality for Following Deformable Linear Objects Using Reinforcement Learning—0
Visual Tracking by means of Deep Reinforcement Learning and an Expert Demonstrator—0
Visuomotor Mechanical Search: Learning to Retrieve Target Objects in Clutter—0
ViVa: Video-Trained Value Functions for Guiding Online RL from Diverse Data—0
Vizarel: A System to Help Better Understand RL Agents—0
VLMLight: Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning—0
VLM Q-Learning: Aligning Vision-Language Models for Interactive Decision-Making—0
VLM-RL: A Unified Vision Language Models and Reinforcement Learning Framework for Safe Autonomous Driving—0
VLP: Vision-Language Preference Learning for Embodied Manipulation—0
VL-SAFE: Vision-Language Guided Safety-Aware Reinforcement Learning with World Models for Autonomous Driving—0
VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control—0
vMFER: Von Mises-Fisher Experience Resampling Based on Uncertainty of Gradient Directions for Policy Improvement—0
VolleyBots: A Testbed for Multi-Drone Volleyball Game Combining Motion Control and Strategic Play—0
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Benchmark Results

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