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 2151–2160 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning for Conservation DecisionsCode1
Deep Reinforcement Learning for Cost-Effective Medical DiagnosisCode1
Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular NetworksCode1
BIMRL: Brain Inspired Meta Reinforcement LearningCode1
Deep Reinforcement Learning for Market Making Under a Hawkes Process-Based Limit Order Book ModelCode1
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
Deep Reinforcement Learning for Producing Furniture Layout in Indoor ScenesCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Boosting Soft Actor-Critic: Emphasizing Recent Experience without Forgetting the PastCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
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Benchmark Results

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