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 2141–2150 of 15113 papers

TitleStatusHype
Distributional Soft Actor-Critic: Off-Policy Reinforcement Learning for Addressing Value Estimation ErrorsCode1
Computational Performance of Deep Reinforcement Learning to find Nash EquilibriaCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Blue River Controls: A toolkit for Reinforcement Learning Control Systems on HardwareCode1
Deep Reinforcement Learning based Recommendation with Explicit User-Item Interactions ModelingCode1
Deep Reinforcement Learning based Evasion Generative Adversarial Network for Botnet DetectionCode1
Deep Reinforcement Learning Control of Quantum CartpolesCode1
Deep Reinforcement Learning for Cryptocurrency Trading: Practical Approach to Address Backtest OverfittingCode1
Deep Reinforcement Learning for Active Human Pose EstimationCode1
Bellman: A Toolbox for Model-Based Reinforcement Learning in TensorFlowCode1
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Benchmark Results

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