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 3111–3120 of 15113 papers

TitleStatusHype
Smart Imitator: Learning from Imperfect Clinical DecisionsCode0
Counterfactually Fair Reinforcement Learning via Sequential Data Preprocessing—0
Investigating the Impact of Observation Space Design Choices On Training Reinforcement Learning Solutions for Spacecraft Problems—0
Real-Time Integrated Dispatching and Idle Fleet Steering with Deep Reinforcement Learning for A Meal Delivery Platform—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
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Benchmark Results

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