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 1571–1580 of 15113 papers

TitleStatusHype
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight DeliveryCode1
MAMBPO: Sample-efficient multi-robot reinforcement learning using learned world modelsCode1
Lyapunov-Regularized Reinforcement Learning for Power System Transient StabilityCode1
Learning the Next Best View for 3D Point Clouds via Topological FeaturesCode1
Continuous Coordination As a Realistic Scenario for Lifelong LearningCode1
Improving Computational Efficiency in Visual Reinforcement Learning via Stored EmbeddingsCode1
The Surprising Effectiveness of PPO in Cooperative, Multi-Agent GamesCode1
Deep Reinforcement Learning for URLLC data management on top of scheduled eMBB trafficCode1
Model-based Constrained Reinforcement Learning using Generalized Control Barrier FunctionCode1
Hierarchical and Partially Observable Goal-driven Policy Learning with Goals Relational GraphCode1
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Benchmark Results

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