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 3541–3550 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning based Model-free On-line Dynamic Multi-Microgrid Formation to Enhance Resilience—0
Deep Reinforcement Learning Based Multi-Access Edge Computing Schedule for Internet of Vehicle—0
Agent based modelling for continuously varying supply chains—0
Deep Reinforcement Learning Based on Location-Aware Imitation Environment for RIS-Aided mmWave MIMO Systems—0
Accelerating the Computation of UCB and Related Indices for Reinforcement Learning—0
Deep Reinforcement Learning based Optimal Control of Hot Water Systems—0
Demand Responsive Dynamic Pricing Framework for Prosumer Dominated Microgrids using Multiagent Reinforcement Learning—0
A Stochastic Composite Augmented Lagrangian Method For Reinforcement Learning—0
Count-Based Temperature Scheduling for Maximum Entropy Reinforcement Learning—0
Delegative Reinforcement Learning: learning to avoid traps with a little help—0
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Benchmark Results

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