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 2701–2710 of 15113 papers

TitleStatusHype
Automatic Face Aging in Videos via Deep Reinforcement Learning—0
Automatic Exploration Process Adjustment for Safe Reinforcement Learning with Joint Chance Constraint Satisfaction—0
Automatic Essay Scoring Incorporating Rating Schema via Reinforcement Learning—0
Automatic Environment Shaping is the Next Frontier in RL—0
Align Your Intents: Offline Imitation Learning via Optimal Transport—0
Adaptive Height Optimisation for Cellular-Connected UAVs using Reinforcement Learning—0
Automatic Document Sketching: Generating Drafts from Analogous Texts—0
Automatic Discovery of Multi-perspective Process Model using Reinforcement Learning—0
Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey—0
Automatic Data Augmentation via Deep Reinforcement Learning for Effective Kidney Tumor Segmentation—0
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Benchmark Results

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