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 3841–3850 of 15113 papers

TitleStatusHype
Discovering Multiple Solutions from a Single Task in Offline Reinforcement Learning—0
Deep Multi-Objective Reinforcement Learning for Utility-Based Infrastructural Maintenance OptimizationCode0
Decoupling regularization from the action spaceCode0
Is Value Functions Estimation with Classification Plug-and-play for Offline Reinforcement Learning?Code0
STARLING: Self-supervised Training of Text-based Reinforcement Learning Agent with Large Language ModelsCode0
Decision Mamba: A Multi-Grained State Space Model with Self-Evolution Regularization for Offline RLCode0
Enhanced Flight Envelope Protection: A Novel Reinforcement Learning Approach—0
Diffusion-based Reinforcement Learning for Dynamic UAV-assisted Vehicle Twins Migration in Vehicular Metaverses—0
Optimizing Automatic Differentiation with Deep Reinforcement Learning—0
Sim-to-Real Transfer of Deep Reinforcement Learning Agents for Online Coverage Path Planning—0
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Benchmark Results

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