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 3431–3440 of 15113 papers

TitleStatusHype
Deep Learning and Reinforcement Learning for Autonomous Unmanned Aerial Systems: Roadmap for Theory to Deployment—0
A unified view of likelihood ratio and reparameterization gradients and an optimal importance sampling scheme—0
A Unifying Framework for Action-Conditional Self-Predictive Reinforcement Learning—0
Deep learning for molecular design - a review of the state of the art—0
Deep Reinforcement Learning Policies Learn Shared Adversarial Features Across MDPs—0
Deep Learning in Earthquake Engineering: A Comprehensive Review—0
Adaptive Dialog Policy Learning with Hindsight and User Modeling—0
Deep Learning Interference Cancellation in Wireless Networks—0
Deep reinforcement learning to detect brain lesions on MRI: a proof-of-concept application of reinforcement learning to medical images—0
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing—0
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Benchmark Results

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