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
Deep Reinforcement Learning in mmW-NOMA: Joint Power Allocation and Hybrid Beamforming—0
Corruption-Robust Algorithms with Uncertainty Weighting for Nonlinear Contextual Bandits and Markov Decision Processes—0
Correlation Priors for Reinforcement Learning—0
A View on Deep Reinforcement Learning in System Optimization—0
Average Reward Reinforcement Learning for Wireless Radio Resource Management—0
A Comparative Analysis of Reinforcement Learning and Conventional Deep Learning Approaches for Bearing Fault Diagnosis—0
Deep Reinforcement Learning Microgrid Optimization Strategy Considering Priority Flexible Demand Side—0
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result—0
A multi-agent reinforcement learning model of reputation and cooperation in human groups—0
Assured RL: Reinforcement Learning with Almost Sure Constraints—0
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Benchmark Results

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