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 3661–3670 of 15113 papers

TitleStatusHype
Deep Reinforcement Learning For Modeling Chit-Chat Dialog With Discrete Attributes—0
Deep Reinforcement Learning for Motion Planning of Mobile Robots—0
Deep Reinforcement Learning for Multi-Resource Multi-Machine Job Scheduling—0
Deep Reinforcement Learning for Multi-objective Optimization—0
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications—0
Deep Decentralized Reinforcement Learning for Cooperative Control—0
Autonomous Industrial Management via Reinforcement Learning: Self-Learning Agents for Decision-Making -- A Review—0
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants—0
Deep Reinforcement Learning for Multi-Driver Vehicle Dispatching and Repositioning Problem—0
Difference of Convex Functions Programming Applied to Control with Expert Data—0
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Benchmark Results

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