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 3651–3660 of 15113 papers

TitleStatusHype
A State Representation Dueling Network for Deep Reinforcement Learning—0
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment—0
Agent-Agnostic Human-in-the-Loop Reinforcement Learning—0
A State Augmentation based approach to Reinforcement Learning from Human Preferences—0
Deep Reinforcement Learning for Long Term Hydropower Production Scheduling—0
Deep Reinforcement Learning for Long-Term Voltage Stability Control—0
Deep reinforcement learning for market making in corporate bonds: beating the curse of dimensionality—0
Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning—0
Dialogue Evaluation with Offline Reinforcement Learning—0
Costate-focused models for reinforcement learning—0
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Benchmark Results

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