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 2231–2240 of 15113 papers

TitleStatusHype
Electric Vehicle Routing Problem for Emergency Power Supply: Towards Telecom Base Station ReliefCode1
Methodology for Interpretable Reinforcement Learning for Optimizing Mechanical Ventilation—0
SliceIt! -- A Dual Simulator Framework for Learning Robot Food SlicingCode0
Reinforcement Learning in Categorical Cybernetics—0
Is Exploration All You Need? Effective Exploration Characteristics for Transfer in Reinforcement Learning—0
Asymptotics of Language Model Alignment—0
EV2Gym: A Flexible V2G Simulator for EV Smart Charging Research and BenchmarkingCode2
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Emergence of Chemotactic Strategies with Multi-Agent Reinforcement Learning—0
MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control—0
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Benchmark Results

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