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 4111–4120 of 15113 papers

TitleStatusHype
Active Vision for Early Recognition of Human Actions—0
Actor-Critic Algorithm for High-dimensional Partial Differential Equations—0
Learning to sample fibers for goodness-of-fit testing—0
Actor-Critic based Improper Reinforcement Learning—0
Actor-Critic Deep Reinforcement Learning for Dynamic Multichannel Access—0
Actor-Critic Deep Reinforcement Learning for Solving Job Shop Scheduling Problems—0
Actor-Critic for Linearly-Solvable Continuous MDP with Partially Known Dynamics—0
Actor-Critic learning for mean-field control in continuous time—0
Actor-Critic Network for O-RAN Resource Allocation: xApp Design, Deployment, and Analysis—0
Actor-Critic Network for Q&A in an Adversarial Environment—0
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Benchmark Results

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