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 2541–2550 of 15113 papers

TitleStatusHype
A Mixture-of-Expert Approach to RL-based Dialogue Management—0
AVDDPG: Federated reinforcement learning applied to autonomous platoon control—0
A Variational Approach to Mutual Information-Based Coordination for Multi-Agent Reinforcement Learning—0
A Mini Review on the utilization of Reinforcement Learning with OPC UA—0
A Comparative Analysis of Machine Learning Techniques for IoT Intrusion Detection—0
A Variant of the Wang-Foster-Kakade Lower Bound for the Discounted Setting—0
A Validation Tool for Designing Reinforcement Learning Environments—0
Adaptive Policy Transfer in Reinforcement Learning—0
A Comparative Analysis of Expected and Distributional Reinforcement Learning—0
Auxiliary Task-based Deep Reinforcement Learning for Quantum Control—0
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Benchmark Results

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