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 2381–2390 of 15113 papers

TitleStatusHype
An Actor-Critic Method for Simulation-Based Optimization—0
Adaptive Stochastic ADMM for Decentralized Reinforcement Learning in Edge Industrial IoT—0
A Comparison of Prediction Algorithms and Nexting for Short Term Weather Forecasts—0
Batch Ensemble for Variance Dependent Regret in Stochastic Bandits—0
An Actor-Critic-Attention Mechanism for Deep Reinforcement Learning in Multi-view Environments—0
Batch-Constrained Reinforcement Learning for Dynamic Distribution Network Reconfiguration—0
Batch-Constrained Distributional Reinforcement Learning for Session-based Recommendation—0
Adaptive Shooting for Bots in First Person Shooter Games Using Reinforcement Learning—0
Batch-Augmented Multi-Agent Reinforcement Learning for Efficient Traffic Signal Optimization—0
An Abstraction-based Method to Check Multi-Agent Deep Reinforcement-Learning Behaviors—0
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Benchmark Results

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