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 1081–1090 of 15113 papers

TitleStatusHype
From discrete-time policies to continuous-time diffusion samplers: Asymptotic equivalences and faster trainingCode1
Real-Time Integrated Dispatching and Idle Fleet Steering with Deep Reinforcement Learning for A Meal Delivery Platform—0
Diffusion Models for Smarter UAVs: Decision-Making and Modeling—0
LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models—0
Deep Transfer Q-Learning for Offline Non-Stationary Reinforcement Learning—0
Risk-averse policies for natural gas futures trading using distributional reinforcement learning—0
Safe Reinforcement Learning with Minimal Supervision—0
Multilinear Tensor Low-Rank Approximation for Policy-Gradient Methods in Reinforcement LearningCode0
Run-and-tumble chemotaxis using reinforcement learning—0
Explainable Reinforcement Learning via Temporal Policy Decomposition—0
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Benchmark Results

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