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 3721–3730 of 15113 papers

TitleStatusHype
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
To the Noise and Back: Diffusion for Shared Autonomy—0
Behavior Proximal Policy OptimizationCode1
Constrained Reinforcement Learning using Distributional Representation for Trustworthy Quadrotor UAV Tracking ControlCode0
Towards Decentralized Predictive Quality of Service in Next-Generation Vehicular Networks—0
Self-supervised network distillation: an effective approach to exploration in sparse reward environmentsCode0
Provably Efficient Reinforcement Learning via Surprise Bound—0
BadGPT: Exploring Security Vulnerabilities of ChatGPT via Backdoor Attacks to InstructGPT—0
Assessment of Reinforcement Learning for Macro PlacementCode2
A Reinforcement Learning Framework for Online Speaker Diarization—0
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Benchmark Results

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