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 4631–4640 of 15113 papers

TitleStatusHype
Learning Generalizable Agents via Saliency-Guided Features Decorrelation—0
Distributional Reinforcement Learning with Online Risk-awareness Adaption—0
DeepQTest: Testing Autonomous Driving Systems with Reinforcement Learning and Real-world Weather DataCode0
Lifelong Learning for Fog Load Balancing: A Transfer Learning Approach—0
Optimal Sequential Decision-Making in Geosteering: A Reinforcement Learning Approach—0
Reinforced UI Instruction Grounding: Towards a Generic UI Task Automation API—0
Improving Offline-to-Online Reinforcement Learning with Q Conditioned State Entropy Exploration—0
Self-Confirming Transformer for Belief-Conditioned Adaptation in Offline Multi-Agent Reinforcement Learning—0
Improving Reinforcement Learning Efficiency with Auxiliary Tasks in Non-Visual Environments: A Comparison—0
AURO: Reinforcement Learning for Adaptive User Retention Optimization in Recommender Systems—0
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Benchmark Results

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