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 631640 of 15113 papers

TitleStatusHype
DeepFreight: Integrating Deep Reinforcement Learning and Mixed Integer Programming for Multi-transfer Truck Freight DeliveryCode1
A deep inverse reinforcement learning approach to route choice modeling with context-dependent rewardsCode1
Deep Active Inference for Partially Observable MDPsCode1
Decoupling Value and Policy for Generalization in Reinforcement LearningCode1
Deep Actor-Critic Learning for Distributed Power Control in Wireless Mobile NetworksCode1
Asset Allocation: From Markowitz to Deep Reinforcement LearningCode1
Decoupling Strategy and Generation in Negotiation DialoguesCode1
Deep Black-Box Reinforcement Learning with Movement PrimitivesCode1
Actor-Attention-Critic for Multi-Agent Reinforcement LearningCode1
Accelerating Quadratic Optimization with Reinforcement LearningCode1
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Benchmark Results

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