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 6326–6350 of 15113 papers

TitleStatusHype
Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training—0
Survey on Fair Reinforcement Learning: Theory and Practice—0
Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods—0
Survey on Multi-Agent Q-Learning frameworks for resource management in wireless sensor network—0
Survey on reinforcement learning for language processing—0
Survey on Strategic Mining in Blockchain: A Reinforcement Learning Approach—0
Surveys without Questions: A Reinforcement Learning Approach—0
Survival Analysis on Structured Data using Deep Reinforcement Learning—0
Survival Instinct in Offline Reinforcement Learning—0
Survival of the Fittest: Evolutionary Adaptation of Policies for Environmental Shifts—0
SVDE: Scalable Value-Decomposition Exploration for Cooperative Multi-Agent Reinforcement Learning—0
SVQN: Sequential Variational Soft Q-Learning Networks—0
Swarm Behavior Cloning—0
Learning from Imperfect Demonstrations with Self-Supervision for Robotic Manipulation—0
SWE-RL: Advancing LLM Reasoning via Reinforcement Learning on Open Software Evolution—0
Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning—0
Switching Isotropic and Directional Exploration with Parameter Space Noise in Deep Reinforcement Learning—0
Switching Linear Dynamics for Variational Bayes Filtering—0
Switching the Loss Reduces the Cost in Batch Reinforcement Learning—0
SwitchMT: An Adaptive Context Switching Methodology for Scalable Multi-Task Learning in Intelligent Autonomous Agents—0
Switch Trajectory Transformer with Distributional Value Approximation for Multi-Task Reinforcement Learning—0
Symbol-Based Over-the-Air Digital Predistortion Using Reinforcement Learning—0
Symbolic Explanation of Affinity-Based Reinforcement Learning Agents with Markov Models—0
Symbolic Regression Methods for Reinforcement Learning—0
Symbolic Reinforcement Learning for Safe RAN Control—0
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Benchmark Results

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