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 4121–4130 of 15113 papers

TitleStatusHype
Actor-Critic Policy Optimization in Partially Observable Multiagent Environments—0
Actor-Critic Reinforcement Learning with Simultaneous Human Control and Feedback—0
Actor-Critic Reinforcement Learning with Phased Actor—0
Actor-Critics Can Achieve Optimal Sample Efficiency—0
Actor-Critic Scheduling for Path-Aware Air-to-Ground Multipath Multimedia Delivery—0
Actor-Critic Sequence Training for Image Captioning—0
Actor Critic with Differentially Private Critic—0
Actor-Critic with variable time discretization via sustained actions—0
Actor-Director-Critic: A Novel Deep Reinforcement Learning Framework—0
ACTRCE: Augmenting Experience via Teacher's Advice For Multi-Goal Reinforcement Learning—0
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Benchmark Results

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