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 2001–2010 of 15113 papers

TitleStatusHype
Sample Efficient Ensemble Learning with Catalyst.RLCode1
Modeling 3D Shapes by Reinforcement LearningCode1
An empirical investigation of the challenges of real-world reinforcement learningCode1
Evolutionary Population Curriculum for Scaling Multi-Agent Reinforcement LearningCode1
Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D EnvironmentsCode1
FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning TechniquesCode1
Enhanced POET: Open-Ended Reinforcement Learning through Unbounded Invention of Learning Challenges and their SolutionsCode1
Robust Deep Reinforcement Learning against Adversarial Perturbations on State ObservationsCode1
Social Navigation with Human Empowerment driven Deep Reinforcement LearningCode1
Giving Up Control: Neurons as Reinforcement Learning AgentsCode1
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Benchmark Results

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