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 1831–1840 of 15113 papers

TitleStatusHype
Reinforcement Learning and Control as Probabilistic Inference: Tutorial and ReviewCode1
Reinforcement Learning and Tree Search Methods for the Unit Commitment ProblemCode1
Reinforcement Learning as One Big Sequence Modeling ProblemCode1
Reinforcement Learning-Based Automatic Berthing SystemCode1
Combining Modular Skills in Multitask LearningCode1
Reinforcement Learning-based Model Predictive Control for Greenhouse Climate ControlCode1
Reinforcement Learning-based Non-Autoregressive Solver for Traveling Salesman ProblemsCode1
Reinforcement Learning-based Placement of Charging Stations in Urban Road NetworksCode1
Reinforcement Learning Enhanced Quantum-inspired Algorithm for Combinatorial OptimizationCode1
Combinatorial Optimization by Graph Pointer Networks and Hierarchical Reinforcement LearningCode1
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Benchmark Results

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