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 4451–4460 of 15113 papers

TitleStatusHype
A Graph Neural Network-Based QUBO-Formulated Hamiltonian-Inspired Loss Function for Combinatorial Optimization using Reinforcement Learning—0
A Graph Policy Network Approach for Volt-Var Control in Power Distribution Systems—0
A gray-box approach for curriculum learning—0
A Greedy Approximation of Bayesian Reinforcement Learning with Probably Optimistic Transition Model—0
A Guider Network for Multi-Dual Learning—0
A Guiding Principle for Causal Decision Problems—0
A Heuristically Assisted Deep Reinforcement Learning Approach for Network Slice Placement—0
A Hierarchical Bayesian Approach to Inverse Reinforcement Learning with Symbolic Reward Machines—0
A Hierarchical Deep Reinforcement Learning Framework for 6-DOF UCAV Air-to-Air Combat—0
A Hierarchical Framework of Cloud Resource Allocation and Power Management Using Deep Reinforcement Learning—0
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Benchmark Results

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