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 3611–3620 of 15113 papers

TitleStatusHype
Cost-Sensitive Exploration in Bayesian Reinforcement Learning—0
Deep Reinforcement Learning for Dynamic Treatment Regimes on Medical Registry Data—0
Deep Reinforcement Learning for Dynamic Spectrum Sensing and Aggregation in Multi-Channel Wireless Networks—0
Deep Reinforcement Learning for Dynamic Spectrum Sharing of LTE and NR—0
A State Representation for Diminishing Rewards—0
Deep Reinforcement Learning for Dynamic Band Switch in Cellular-Connected UAV—0
CostNet: An End-to-End Framework for Goal-Directed Reinforcement Learning—0
Deep Reinforcement Learning for Dynamic Urban Transportation Problems—0
Cost-Effective Two-Stage Network Slicing for Edge-Cloud Orchestrated Vehicular Networks—0
A State Representation Dueling Network for Deep Reinforcement Learning—0
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Benchmark Results

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