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 3261–3270 of 15113 papers

TitleStatusHype
Data-pooling Reinforcement Learning for Personalized Healthcare Intervention—0
Data Quality-aware Mixed-precision Quantization via Hybrid Reinforcement Learning—0
Creating Pro-Level AI for a Real-Time Fighting Game Using Deep Reinforcement Learning—0
Creating a Dynamic Quadrupedal Robotic Goalkeeper with Reinforcement Learning—0
A Surrogate-Assisted Controller for Expensive Evolutionary Reinforcement Learning—0
A Temporal Difference Reinforcement Learning Theory of Emotion: unifying emotion, cognition and adaptive behavior—0
Agent Modeling as Auxiliary Task for Deep Reinforcement Learning—0
Data Valuation for Offline Reinforcement Learning—0
A Tensor Network Approach to Finite Markov Decision Processes—0
A SUMO Framework for Deep Reinforcement Learning Experiments Solving Electric Vehicle Charging Dispatching Problem—0
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Benchmark Results

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