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 3731–3740 of 15113 papers

TitleStatusHype
Adversarial Model for Offline Reinforcement Learning—0
Reinforcement Learning for Block Decomposition of CAD Models—0
Robust Auto-landing Control of an agile Regional Jet Using Fuzzy Q-learning—0
Minimax-Bayes Reinforcement LearningCode0
Learning to Play Text-based Adventure Games with Maximum Entropy Reinforcement LearningCode0
Deep Reinforcement Learning for Robotic Pushing and Picking in Cluttered Environment—0
Provably Efficient Exploration in Quantum Reinforcement Learning with Logarithmic Worst-Case Regret—0
Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space—0
Curiosity-driven Exploration in Sparse-reward Multi-agent Reinforcement Learning—0
Towards a Sustainable Internet-of-Underwater-Things based on AUVs, SWIPT, and Reinforcement Learning—0
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Benchmark Results

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