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 1441–1450 of 15113 papers

TitleStatusHype
Deep Laplacian-based Options for Temporally-Extended ExplorationCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character SkillsCode1
Deep Reinforcement Learning at the Edge of the Statistical PrecipiceCode1
A Scalable and Reproducible System-on-Chip Simulation for Reinforcement LearningCode1
FedFormer: Contextual Federation with Attention in Reinforcement LearningCode1
Fine-Grained Causal Dynamics Learning with Quantization for Improving Robustness in Reinforcement LearningCode1
DeepMind Lab2DCode1
BabyAI 1.1Code1
A Deep Reinforcement Learning Algorithm Using Dynamic Attention Model for Vehicle Routing ProblemsCode1
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Benchmark Results

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