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 3711–3720 of 15113 papers

TitleStatusHype
Accelerating Stochastic Composition Optimization—0
Corruption-Robust Offline Reinforcement Learning—0
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment—0
Deep Reinforcement Learning for Robotics: A Survey of Real-World Successes—0
Deep Reinforcement Learning for Routing a Heterogeneous Fleet of Vehicles—0
Deep Reinforcement Learning for Safe Landing Site Selection with Concurrent Consideration of Divert Maneuvers—0
Deep Reinforcement Learning for Scalable Multiagent Spacecraft Inspection—0
Using Deep Reinforcement Learning for mmWave Real-Time Scheduling—0
Deep reinforcement learning for scheduling in large-scale networked control systems—0
Corruption-robust exploration in episodic reinforcement learning—0
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Benchmark Results

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