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

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Showing 3521–3530 of 15113 papers

TitleStatusHype
A random measure approach to reinforcement learning in continuous time—0
Spiders Based on Anxiety: How Reinforcement Learning Can Deliver Desired User Experience in Virtual Reality Personalized Arachnophobia TreatmentCode0
Whole-body End-Effector Pose Tracking—0
Reinforcement Leaning for Infinite-Dimensional Systems—0
Development and Validation of Heparin Dosing Policies Using an Offline Reinforcement Learning Algorithm—0
From Goal-Conditioned to Language-Conditioned Agents via Vision-Language Models—0
Physics Enhanced Residual Policy Learning (PERPL) for safety cruising in mixed traffic platooning under actuator and communication delay—0
Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach—0
A novel agent with formal goal-reaching guarantees: an experimental study with a mobile robot—0
CANDERE-COACH: Reinforcement Learning from Noisy Feedback—0
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Benchmark Results

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