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 2041–2050 of 15113 papers

TitleStatusHype
Adaptive Q-Network: On-the-fly Target Selection for Deep Reinforcement Learning—0
AIGB: Generative Auto-bidding via Conditional Diffusion Modeling—0
Embedding-Aligned Language Models—0
SF-DQN: Provable Knowledge Transfer using Successor Feature for Deep Reinforcement Learning—0
Knowledge-Informed Auto-Penetration Testing Based on Reinforcement Learning with Reward Machine—0
Diffusion Actor-Critic with Entropy RegulatorCode2
Extracting Heuristics from Large Language Models for Reward Shaping in Reinforcement Learning—0
Model-free reinforcement learning with noisy actions for automated experimental control in opticsCode0
Human-in-the-loop Reinforcement Learning for Data Quality Monitoring in Particle Physics Experiments—0
Cooperative Backdoor Attack in Decentralized Reinforcement Learning with Theoretical Guarantee—0
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Benchmark Results

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