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 1291–1300 of 15113 papers

TitleStatusHype
Adversarial Deep Reinforcement Learning for Improving the Robustness of Multi-agent Autonomous Driving PoliciesCode1
Echo Chamber: RL Post-training Amplifies Behaviors Learned in PretrainingCode1
Adversarial Deep Reinforcement Learning in Portfolio ManagementCode1
Edge Rewiring Goes Neural: Boosting Network Resilience without Rich FeaturesCode1
An Experimental Design Perspective on Model-Based Reinforcement LearningCode1
A reinforcement learning path planning approach for range-only underwater target localization with autonomous vehiclesCode1
Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement LearningCode1
Efficient Continuous Control with Double Actors and Regularized CriticsCode1
A Crash Course on Reinforcement LearningCode1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
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Benchmark Results

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