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 1081–1090 of 15113 papers

TitleStatusHype
Mildly Conservative Q-Learning for Offline Reinforcement LearningCode1
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement LearningCode1
Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement LearningCode1
Stabilizing Voltage in Power Distribution Networks via Multi-Agent Reinforcement Learning with TransformerCode1
How Far I'll Go: Offline Goal-Conditioned Reinforcement Learning via f-Advantage RegressionCode1
RORL: Robust Offline Reinforcement Learning via Conservative SmoothingCode1
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate ProgressCode1
Deep Transformer Q-Networks for Partially Observable Reinforcement LearningCode1
NeuralSympCheck: A Symptom Checking and Disease Diagnostic Neural Model with Logic RegularizationCode1
When does return-conditioned supervised learning work for offline reinforcement learning?Code1
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Benchmark Results

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