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 4681–4690 of 15113 papers

TitleStatusHype
Tempo Adaptation in Non-stationary Reinforcement LearningCode0
ODE-based Recurrent Model-free Reinforcement Learning for POMDPs—0
Tracking Control for a Spherical Pendulum via Curriculum Reinforcement Learning—0
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
A comparison of controller architectures and learning mechanisms for arbitrary robot morphologies—0
Boosting Offline Reinforcement Learning for Autonomous Driving with Hierarchical Latent Skills—0
Guided Cooperation in Hierarchical Reinforcement Learning via Model-based RolloutCode0
Iterative Reachability Estimation for Safe Reinforcement Learning—0
Limits of Actor-Critic Algorithms for Decision Tree Policies Learning in IBMDPs—0
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Benchmark Results

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