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 3301–3310 of 15113 papers

TitleStatusHype
Deep reinforcement learning for optimal well control in subsurface systems with uncertain geology—0
Decentralized Deep Reinforcement Learning for Network Level Traffic Signal Control—0
Deep Reinforcement Learning for Organ Localization in CT—0
Decentralized Distributed Proximal Policy Optimization (DD-PPO) for High Performance Computing Scheduling on Multi-User Systems—0
Decentralized Federated Reinforcement Learning for User-Centric Dynamic TFDD Control—0
Decentralized Global Connectivity Maintenance for Multi-Robot Navigation: A Reinforcement Learning Approach—0
Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks—0
Decentralized Graph-Based Multi-Agent Reinforcement Learning Using Reward Machines—0
CQM: Curriculum Reinforcement Learning with a Quantized World Model—0
AgentGraph: Towards Universal Dialogue Management with Structured Deep Reinforcement Learning—0
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Benchmark Results

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