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 3361–3370 of 15113 papers

TitleStatusHype
Resource Governance in Networked Systems via Integrated Variational Autoencoders and Reinforcement Learning—0
Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning—0
A Multi-Agent Reinforcement Learning Testbed for Cognitive Radio Applications—0
Getting By Goal Misgeneralization With a Little Help From a Mentor—0
The Limits of Transfer Reinforcement Learning with Latent Low-rank Structure—0
Bilevel Model for Electricity Market Mechanism Optimisation via Quantum Computing Enhanced Reinforcement Learning—0
FairStream: Fair Multimedia Streaming Benchmark for Reinforcement Learning AgentsCode0
GFlowNet Fine-tuning for Diverse Correct Solutions in Mathematical Reasoning Tasks—0
Off-Policy Selection for Initiating Human-Centric Experimental Design—0
Beyond Simple Sum of Delayed Rewards: Non-Markovian Reward Modeling for Reinforcement Learning—0
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Benchmark Results

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