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 2911–2920 of 15113 papers

TitleStatusHype
Decentralized Multi-Agent Reinforcement Learning for Task Offloading Under Uncertainty—0
Deceptive Reinforcement Learning for Privacy-Preserving Planning—0
On the Theory of Risk-Aware Agents: Bridging Actor-Critic and Economics—0
Attention-Aware Face Hallucination via Deep Reinforcement Learning—0
Attention-Aware Deep Reinforcement Learning for Video Face Recognition—0
AI-based Robust Resource Allocation in End-to-End Network Slicing under Demand and CSI Uncertainties—0
Attentional Policies for Cross-Context Multi-Agent Reinforcement Learning—0
AI-based Resource Allocation: Reinforcement Learning for Adaptive Auto-scaling in Serverless Environments—0
AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control—0
Attend2Pack: Bin Packing through Deep Reinforcement Learning with Attention—0
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Benchmark Results

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