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 2121–2130 of 15113 papers

TitleStatusHype
Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate ProgressCode1
Curriculum-based Reinforcement Learning for Distribution System Critical Load RestorationCode1
D2RL: Deep Dense Architectures in Reinforcement LearningCode1
Comparing Observation and Action Representations for Deep Reinforcement Learning in μRTSCode1
Beyond The Rainbow: High Performance Deep Reinforcement Learning on a Desktop PCCode1
Beyond Uniform Sampling: Offline Reinforcement Learning with Imbalanced DatasetsCode1
Combining Modular Skills in Multitask LearningCode1
Bingham Policy Parameterization for 3D Rotations in Reinforcement LearningCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Combining Reinforcement Learning and Constraint Programming for Combinatorial OptimizationCode1
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Benchmark Results

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