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 3951–3960 of 15113 papers

TitleStatusHype
A Multimodal Learning-based Approach for Autonomous Landing of UAV—0
Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationing—0
Rethinking Robustness Assessment: Adversarial Attacks on Learning-based Quadrupedal Locomotion Controllers—0
Practical and efficient quantum circuit synthesis and transpiling with Reinforcement Learning—0
Highway Graph to Accelerate Reinforcement LearningCode0
Learning Future Representation with Synthetic Observations for Sample-efficient Reinforcement Learning—0
Scrutinize What We Ignore: Reining In Task Representation Shift Of Context-Based Offline Meta Reinforcement LearningCode0
Investigating the Impact of Choice on Deep Reinforcement Learning for Space Controls—0
Large Language Models are Biased Reinforcement LearnersCode0
Do No Harm: A Counterfactual Approach to Safe Reinforcement Learning—0
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Benchmark Results

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