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 3391–3400 of 15113 papers

TitleStatusHype
Accelerating the Learning of TAMER with Counterfactual Explanations—0
Deep Reinforcement Learning in Computer Vision: A Comprehensive Survey—0
Augmenting Automated Game Testing with Deep Reinforcement Learning—0
DeepCQ+: Robust and Scalable Routing with Multi-Agent Deep Reinforcement Learning for Highly Dynamic Networks—0
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games—0
Deep Curiosity Loops in Social Environments—0
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability—0
Deep Reinforcement Learning in mmW-NOMA: Joint Power Allocation and Hybrid Beamforming—0
Deep differentiable reinforcement learning and optimal trading—0
Deep Reinforcement Learning Models Predict Visual Responses in the Brain: A Preliminary Result—0
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Benchmark Results

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