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 4661–4670 of 15113 papers

TitleStatusHype
Navigating Uncertainty in ESG Investing—0
Towards a Unified Framework for Sequential Decision Making—0
Pessimistic Nonlinear Least-Squares Value Iteration for Offline Reinforcement Learning—0
REMEDI: REinforcement learning-driven adaptive MEtabolism modeling of primary sclerosing cholangitis DIsease progression—0
Improving Dialogue Management: Quality Datasets vs ModelsCode0
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information—0
Controlling Neural Style Transfer with Deep Reinforcement Learning—0
Adaptive Control of an Inverted Pendulum by a Reinforcement Learning-based LQR Method—0
A Quantum States Preparation Method Based on Difference-Driven Reinforcement Learning—0
Learning to Rewrite Prompts for Personalized Text Generation—0
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Benchmark Results

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