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

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Showing 4501–4510 of 15113 papers

TitleStatusHype
From Images to Connections: Can DQN with GNNs learn the Strategic Game of Hex?Code0
Probabilistic Inference in Reinforcement Learning Done Right—0
Risk-sensitive Markov Decision Process and Learning under General Utility Functions—0
Resilient Control of Networked Microgrids using Vertical Federated Reinforcement Learning: Designs and Real-Time Test-Bed Validations—0
Analyzing Behaviors of Mixed Traffic via Reinforcement Learning at Unsignalized Intersections—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
Offline Reinforcement Learning for Wireless Network Optimization with Mixture Datasets—0
Tactile Active Inference Reinforcement Learning for Efficient Robotic Manipulation Skill Acquisition—0
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Benchmark Results

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