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 3691–3700 of 15113 papers

TitleStatusHype
Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning—0
Deep Reinforcement Learning for Personalized Search Story Recommendation—0
Autonomous Quadrotor Landing using Deep Reinforcement Learning—0
Autonomous Platoon Control with Integrated Deep Reinforcement Learning and Dynamic Programming—0
Costate-focused models for reinforcement learning—0
Deep Reinforcement Learning for Power Control in Next-Generation WiFi Network Systems—0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning—0
Deep Reinforcement Learning for Process Control: A Primer for Beginners—0
Accelerating Stochastic Composition Optimization—0
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Benchmark Results

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