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 1391–1400 of 15113 papers

TitleStatusHype
Sample Efficient Reinforcement Learning via Large Vision Language Model DistillationCode1
Generalizable Visual Reinforcement Learning with Segment Anything ModelCode1
Generalization in Reinforcement Learning by Soft Data AugmentationCode1
A Relational Intervention Approach for Unsupervised Dynamics Generalization in Model-Based Reinforcement LearningCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Combining Deep Reinforcement Learning and Search for Imperfect-Information GamesCode1
Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal ReasoningCode1
Combining Modular Skills in Multitask LearningCode1
Reinforcement Learning for Combining Search Methods in the Calibration of Economic ABMsCode1
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Benchmark Results

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