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 3551–3560 of 15113 papers

TitleStatusHype
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning—0
Deep Reinforcement Learning-BasedRobust Protection in DER-Rich Distribution Grids—0
Deep-Reinforcement-Learning-Based Scheduling with Contiguous Resource Allocation for Next-Generation Cellular Systems—0
Deep Reinforcement Learning Based Semi-Autonomous Control for Robotic Surgery—0
Deep Reinforcement Learning-based Text Anonymization against Private-Attribute Inference—0
Deep Reinforcement Learning Boosted by External Knowledge—0
Deep Reinforcement Learning Control for Radar Detection and Tracking in Congested Spectral Environments—0
Deep Reinforcement Learning Controller for 3D Path-following and Collision Avoidance by Autonomous Underwater Vehicles—0
Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning—0
Agent-Aware Dropout DQN for Safe and Efficient On-line Dialogue Policy Learning—0
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Benchmark Results

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