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 4441–4450 of 15113 papers

TitleStatusHype
Wind Power Forecasting Considering Data Privacy Protection: A Federated Deep Reinforcement Learning Approach—0
Over-communicate no more: Situated RL agents learn concise communication protocols—0
Spatial-temporal recurrent reinforcement learning for autonomous shipsCode1
Offline RL With Realistic Datasets: Heteroskedasticity and Support Constraints—0
Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network—0
Dual Generator Offline Reinforcement Learning—0
Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems—0
Learning to Grasp the Ungraspable with Emergent Extrinsic Dexterity—0
Behavior Prior Representation learning for Offline Reinforcement LearningCode0
DynamicLight: Two-Stage Dynamic Traffic Signal TimingCode0
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Benchmark Results

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