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 4531–4540 of 15113 papers

TitleStatusHype
Alternating Good-for-MDP Automata—0
Alternative Function Approximation Parameterizations for Solving Games: An Analysis of f-Regression Counterfactual Regret Minimization—0
AltGraph: Redesigning Quantum Circuits Using Generative Graph Models for Efficient Optimization—0
A Lyapunov Drift-Plus-Penalty Method Tailored for Reinforcement Learning with Queue Stability—0
A Lyapunov Theory for Finite-Sample Guarantees of Asynchronous Q-Learning and TD-Learning Variants—0
A Machine Learning Approach for Prosumer Management in Intraday Electricity Markets—0
A Machine Learning Approach for Task and Resource Allocation in Mobile Edge Computing Based Networks—0
A Machine Learning Approach to Routing—0
A Machine of Few Words -- Interactive Speaker Recognition with Reinforcement Learning—0
A Maintenance Planning Framework using Online and Offline Deep Reinforcement Learning—0
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Benchmark Results

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