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 4726–4750 of 15113 papers

TitleStatusHype
A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances—0
A Novel Deep Reinforcement Learning Based Stock Direction Prediction using Knowledge Graph and Community Aware Sentiments—0
A Novel Deep Reinforcement Learning Based Automated Stock Trading System Using Cascaded LSTM Networks—0
A Novel Deep Reinforcement Learning-based Approach for Enhancing Spectral Efficiency of IRS-assisted Wireless Systems—0
A Novel Entropy-Maximizing TD3-based Reinforcement Learning for Automatic PID Tuning—0
A Novel Experts Advice Aggregation Framework Using Deep Reinforcement Learning for Portfolio Management—0
A Novel Framework for Neural Architecture Search in the Hill Climbing Domain—0
A Novel Multi-Agent Deep RL Approach for Traffic Signal Control—0
A Novel Multi-Objective Reinforcement Learning Algorithm for Pursuit-Evasion Game—0
A Novel Neuromorphic Processors Realization of Spiking Deep Reinforcement Learning for Portfolio Management—0
A Novel Reinforcement Learning Model for Post-Incident Malware Investigations—0
A novel repetition normalized adversarial reward for headline generation—0
A Novel Stochastic Gradient Descent Algorithm for Learning Principal Subspaces—0
An overall view of key problems in algorithmic trading and recent progress—0
An Overview of Machine Learning-Enabled Optimization for Reconfigurable Intelligent Surfaces-Aided 6G Networks: From Reinforcement Learning to Large Language Models—0
An Overview of Natural Language State Representation for Reinforcement Learning—0
An RL-Based Adaptive Detection Strategy to Secure Cyber-Physical Systems—0
ANS: Adaptive Network Scaling for Deep Rectifier Reinforcement Learning Models—0
Answer-driven Deep Question Generation based on Reinforcement Learning—0
Answer Set Programming for Non-Stationary Markov Decision Processes—0
Answer-Supervised Question Reformulation for Enhancing Conversational Machine Comprehension—0
Emotional Contagion-Aware Deep Reinforcement Learning for Antagonistic Crowd Simulation—0
Anti-Concentrated Confidence Bonuses for Scalable Exploration—0
Antifragile Perimeter Control: Anticipating and Gaining from Disruptions with Reinforcement Learning—0
Anti-Overestimation Dialogue Policy Learning for Task-Completion Dialogue System—0
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Benchmark Results

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