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 3781–3790 of 15113 papers

TitleStatusHype
Reinforcement Learning Based Power Grid Day-Ahead Planning and AI-Assisted Control—0
Meta-Reinforcement Learning via Exploratory Task Clustering—0
Deep Offline Reinforcement Learning for Real-world Treatment Optimization Applications—0
Scalable Multi-Agent Reinforcement Learning with General Utilities—0
Prioritized offline Goal-swapping Experience Replay—0
CERiL: Continuous Event-based Reinforcement Learning—0
Optimal Sample Complexity of Reinforcement Learning for Mixing Discounted Markov Decision Processes—0
To Risk or Not to Risk: Learning with Risk Quantification for IoT Task Offloading in UAVs—0
Deep Reinforcement Learning for Multi-user Massive MIMO with Channel Aging—0
Learning a model is paramount for sample efficiency in reinforcement learning control of PDEsCode0
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Benchmark Results

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