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 2441–2450 of 15113 papers

TitleStatusHype
ReinFlow: Fine-tuning Flow Matching Policy with Online Reinforcement Learning—0
Finite-Sample Convergence Bounds for Trust Region Policy Optimization in Mean-Field Games—0
A Provable Approach for End-to-End Safe Reinforcement Learning—0
Enhancing Study-Level Inference from Clinical Trial Papers via RL-based Numeric Reasoning—0
HDDLGym: A Tool for Studying Multi-Agent Hierarchical Problems Defined in HDDL with OpenAI GymCode0
Maximizing Confidence Alone Improves Reasoning—0
Decomposing Elements of Problem Solving: What "Math" Does RL Teach?Code0
SOReL and TOReL: Two Methods for Fully Offline Reinforcement LearningCode0
When Does Neuroevolution Outcompete Reinforcement Learning in Transfer Learning Tasks?Code0
Rendering-Aware Reinforcement Learning for Vector Graphics Generation—0
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Benchmark Results

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