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 2741–2750 of 15113 papers

TitleStatusHype
From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?Code0
Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections—0
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations—0
Clustered Policy Decision Ranking—0
Reinforcement Learning and Deep Stochastic Optimal Control for Final Quadratic Hedging—0
Provably Efficient CVaR RL in Low-rank MDPs—0
Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition—0
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets—0
Benchmarking Feature Extractors for Reinforcement Learning-Based Semiconductor Defect Localization—0
Imagination-Augmented Hierarchical Reinforcement Learning for Safe and Interactive Autonomous Driving in Urban Environments—0
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Benchmark Results

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