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

Showing 15711580 of 15113 papers

TitleStatusHype
Collision Probability Distribution Estimation via Temporal Difference LearningCode1
Abstract-to-Executable Trajectory Translation for One-Shot Task GeneralizationCode1
Local policy search with Bayesian optimizationCode1
Logically-Constrained Reinforcement LearningCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
Learning to combine primitive skills: A step towards versatile robotic manipulationCode1
Look Beneath the Surface: Exploiting Fundamental Symmetry for Sample-Efficient Offline RLCode1
Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement LearningCode1
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement LearningCode1
An End-to-End Reinforcement Learning Approach for Job-Shop Scheduling Problems Based on Constraint ProgrammingCode1
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Benchmark Results

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