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 1411–1420 of 15113 papers

TitleStatusHype
Reinforcement Learning for Dynamic Memory AllocationCode0
Training Language Models to Critique With Multi-agent Feedback—0
MENTOR: Mixture-of-Experts Network with Task-Oriented Perturbation for Visual Reinforcement Learning—0
IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement LearningCode2
Augmented Lagrangian-Based Safe Reinforcement Learning Approach for Distribution System Volt/VAR Control—0
A Novel Reinforcement Learning Model for Post-Incident Malware Investigations—0
Action abstractions for amortized sampling—0
Reinforcement Learning in Non-Markov Market-Making—0
Harnessing Causality in Reinforcement Learning With Bagged Decision Times—0
A Large Language Model-Driven Reward Design Framework via Dynamic Feedback for Reinforcement Learning—0
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Benchmark Results

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