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 6426–6450 of 15113 papers

TitleStatusHype
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning—0
A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning—0
Inherently Explainable Reinforcement Learning in Natural Language—0
Learning from Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning—0
Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection—0
Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management—0
Block Policy Mirror Descent—0
Profitable Strategy Design by Using Deep Reinforcement Learning for Trades on Cryptocurrency Markets—0
Recursive Least Squares Advantage Actor-Critic Algorithms—0
Reinforcement Learning based Air Combat Maneuver Generation—0
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
Demystifying Reinforcement Learning in Time-Varying Systems—0
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning—0
Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement LearningCode0
Solving Dynamic Graph Problems with Multi-Attention Deep Reinforcement LearningCode1
Weakly Supervised Scene Text Detection using Deep Reinforcement LearningCode0
Direct Mutation and Crossover in Genetic Algorithms Applied to Reinforcement Learning Tasks—0
Automated Reinforcement Learning: An Overview—0
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
Agent-Temporal Attention for Reward Redistribution in Episodic Multi-Agent Reinforcement LearningCode1
Dyna-T: Dyna-Q and Upper Confidence Bounds Applied to Trees—0
Toddler-Guidance Learning: Impacts of Critical Period on Multimodal AI Agents—0
The Recurrent Reinforcement Learning Crypto Agent—0
Multi-echelon Supply Chains with Uncertain Seasonal Demands and Lead Times Using Deep Reinforcement Learning—0
Task Independent Capsule-Based Agents for Deep Q-Learning—0
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Benchmark Results

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