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 4511–4520 of 15113 papers

TitleStatusHype
A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN—0
AlignDiff: Aligning Diverse Human Preferences via Behavior-Customisable Diffusion Model—0
PARL: A Unified Framework for Policy Alignment in Reinforcement Learning from Human Feedback—0
Aligning Humans and Robots via Reinforcement Learning from Implicit Human Feedback—0
Aligning Language Models with Offline Learning from Human Feedback—0
Alignment and Safety of Diffusion Models via Reinforcement Learning and Reward Modeling: A Survey—0
Align Your Intents: Offline Imitation Learning via Optimal Transport—0
All Roads Lead to Likelihood: The Value of Reinforcement Learning in Fine-Tuning—0
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition—0
Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition—0
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Benchmark Results

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