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 2901–2910 of 15113 papers

TitleStatusHype
Quantitative Resilience Modeling for Autonomous Cyber Defense—0
All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning—0
Accelerating Multi-Task Temporal Difference Learning under Low-Rank Representation—0
Active Alignments of Lens Systems with Reinforcement Learning—0
Adversarial Agents: Black-Box Evasion Attacks with Reinforcement Learning—0
What's Behind PPO's Collapse in Long-CoT? Value Optimization Holds the Secret—0
Minimax Optimal Reinforcement Learning with Quasi-Optimism—0
Quality-Driven Curation of Remote Sensing Vision-Language Data via Learned Scoring Models—0
Scalable Reinforcement Learning for Virtual Machine Scheduling—0
Towards Understanding the Benefit of Multitask Representation Learning in Decision Process—0
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Benchmark Results

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