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 3701–3710 of 15113 papers

TitleStatusHype
Diffusion Self-Weighted Guidance for Offline Reinforcement Learning—0
Deep Reinforcement Learning for QoS-Constrained Resource Allocation in Multiservice Networks—0
Costate-focused models for reinforcement learning—0
Deep reinforcement learning for RAN optimization and control—0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
Diffusion Spectral Representation for Reinforcement Learning—0
Deep Reinforcement Learning for Real-Time Ground Delay Program Revision and Corresponding Flight Delay Assignments—0
Deep Reinforcement Learning for Resource Management in Network Slicing—0
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning—0
Accelerating Stochastic Composition Optimization—0
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Benchmark Results

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