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 7176–7200 of 15113 papers

TitleStatusHype
Versatile Inverse Reinforcement Learning via Cumulative Rewards—0
Vessel-following model for inland waterways based on deep reinforcement learning—0
ViaRL: Adaptive Temporal Grounding via Visual Iterated Amplification Reinforcement Learning—0
Video Captioning via Hierarchical Reinforcement Learning—0
Video-Enhanced Offline Reinforcement Learning: A Model-Based Approach—0
Video-RTS: Rethinking Reinforcement Learning and Test-Time Scaling for Efficient and Enhanced Video Reasoning—0
Video Summarisation by Classification with Deep Reinforcement Learning—0
Video Summarization through Reinforcement Learning with a 3D Spatio-Temporal U-Net—0
Video Violence Recognition and Localization Using a Semi-Supervised Hard Attention Model—0
Viewport-Aware Deep Reinforcement Learning Approach for 360^o Video Caching—0
ViNG: Learning Open-World Navigation with Visual Goals—0
VIPO: Value Function Inconsistency Penalized Offline Reinforcement Learning—0
Virtual Action Actor-Critic Framework for Exploration (Student Abstract)—0
Virtual Autonomous Driving with Reinforcement Learning—0
Virtual Experience to Real World Application: Sidewalk Obstacle Avoidance Using Reinforcement Learning for Visually Impaired—0
Virtuously Safe Reinforcement Learning—0
ViSaRL: Visual Reinforcement Learning Guided by Human Saliency—0
Visceral Machines: Reinforcement Learning with Intrinsic Physiological Rewards—0
Vision-Based Mobile Robotics Obstacle Avoidance With Deep Reinforcement Learning—0
Vision-Language Models Provide Promptable Representations for Reinforcement Learning—0
Visual Backtracking Teleoperation: A Data Collection Protocol for Offline Image-Based Reinforcement Learning—0
Visual Comfort Aware-Reinforcement Learning for Depth Adjustment of Stereoscopic 3D Images—0
Visual Diagnostics for Deep Reinforcement Learning Policy Development—0
VisualEnv: visual Gym environments with Blender—0
Visual Explanation using Attention Mechanism in Actor-Critic-based Deep Reinforcement Learning—0
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Benchmark Results

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