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 2961–2970 of 15113 papers

TitleStatusHype
PlotMap: Automated Layout Design for Building Game WorldsCode0
Zero-Shot Reinforcement Learning from Low Quality DataCode1
Recurrent Hypernetworks are Surprisingly Strong in Meta-RLCode1
A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies—0
Enhancing data efficiency in reinforcement learning: a novel imagination mechanism based on mesh information propagationCode1
Sample Complexity of Neural Policy Mirror Descent for Policy Optimization on Low-Dimensional Manifolds—0
On the Effectiveness of Adversarial Samples against Ensemble Learning-based Windows PE Malware Detectors—0
Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning—0
ODE-based Recurrent Model-free Reinforcement Learning for POMDPs—0
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based RolloutCode0
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Benchmark Results

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