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 681–690 of 15113 papers

TitleStatusHype
Asynchronous Reinforcement Learning for Real-Time Control of Physical RobotsCode1
Adversarial Soft Advantage Fitting: Imitation Learning without Policy OptimizationCode1
Deep Reinforcement Learning for Entity AlignmentCode1
A Text-based Deep Reinforcement Learning Framework for Interactive RecommendationCode1
Deep Reinforcement Learning for Active High Frequency TradingCode1
A Traffic Light Dynamic Control Algorithm with Deep Reinforcement Learning Based on GNN PredictionCode1
AWAC: Accelerating Online Reinforcement Learning with Offline DatasetsCode1
Attention Actor-Critic algorithm for Multi-Agent Constrained Co-operative Reinforcement LearningCode1
Deep Reinforcement Learning for Process SynthesisCode1
Deep Reinforcement Learning for Band Selection in Hyperspectral Image ClassificationCode1
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Benchmark Results

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