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 3151–3160 of 15113 papers

TitleStatusHype
Curiosity-driven Reinforcement Learning for Diverse Visual Paragraph Generation—0
Curiosity-driven reinforcement learning with homeostatic regulation—0
Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning—0
A Survey on Deep Reinforcement Learning for Data Processing and Analytics—0
A Survey on Dialog Management: Recent Advances and Challenges—0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
Deep Reinforcement Learning Boosted by External Knowledge—0
Curious iLQR: Resolving Uncertainty in Model-based RL—0
CROPS: A Deployable Crop Management System Over All Possible State Availabilities—0
A Survey of Demonstration Learning—0
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Benchmark Results

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