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 2991–3000 of 15113 papers

TitleStatusHype
Autonomous and Human-Driven Vehicles Interacting in a Roundabout: A Quantitative and Qualitative Evaluation—0
A Spiking Binary Neuron -- Detector of Causal Links—0
Reward Engineering for Generating Semi-structured ExplanationCode0
Proximal Bellman mappings for reinforcement learning and their application to robust adaptive filtering—0
Physics-constrained robust learning of open-form partial differential equations from limited and noisy dataCode1
Physically Plausible Full-Body Hand-Object Interaction Synthesis—0
Equivariant Data Augmentation for Generalization in Offline Reinforcement Learning—0
VAPOR: Legged Robot Navigation in Outdoor Vegetation Using Offline Reinforcement LearningCode1
Harnessing Deep Q-Learning for Enhanced Statistical Arbitrage in High-Frequency Trading: A Comprehensive Exploration—0
A Real-World Quadrupedal Locomotion Benchmark for Offline Reinforcement Learning—0
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Benchmark Results

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