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 7051–7075 of 15113 papers

TitleStatusHype
Unveiling the Significance of Toddler-Inspired Reward Transition in Goal-Oriented Reinforcement Learning—0
Improved Monte Carlo tree search formulation with multiple root nodes for discrete sizing optimization of truss structures—0
UPDeT: Universal Multi-agent RL via Policy Decoupling with Transformers—0
Upper and Lower Bounds for Distributionally Robust Off-Dynamics Reinforcement Learning—0
Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss—0
Upside-Down Reinforcement Learning for More Interpretable Optimal Control—0
Urban-Focused Multi-Task Offline Reinforcement Learning with Contrastive Data Sharing—0
User-Guided Personalized Image Aesthetic Assessment based on Deep Reinforcement Learning—0
User-Interactive Offline Reinforcement Learning—0
User-Oriented Robust Reinforcement Learning—0
User Tampering in Reinforcement Learning Recommender Systems—0
Using a Deep Reinforcement Learning Agent for Traffic Signal Control—0
Using Chatbots to Teach Languages—0
Using Cognitive Models to Train Warm Start Reinforcement Learning Agents for Human-Computer Interactions—0
Using Contrastive Samples for Identifying and Leveraging Possible Causal Relationships in Reinforcement Learning—0
Using Cyber Terrain in Reinforcement Learning for Penetration Testing—0
Using Deep Reinforcement Learning for the Continuous Control of Robotic Arms—0
Using Deep Reinforcement Learning for Zero Defect Smart Forging—0
Using Deep Reinforcement Learning to Enhance Channel Sampling Patterns in Integrated Sensing and Communication—0
Using Deep Reinforcement Learning to Generate Rationales for Molecules—0
Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem—0
Using Deep Reinforcement Learning to solve Optimal Power Flow problem with generator failures—0
Using Enhanced Gaussian Cross-Entropy in Imitation Learning to Digging the First Diamond in Minecraft—0
Using Experience Classification for Training Non-Markovian Tasks—0
Using General Value Functions to Learn Domain-Backed Inventory Management Policies—0
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Benchmark Results

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