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 3851–3860 of 15113 papers

TitleStatusHype
Assume-Guarantee Reinforcement Learning—0
Deep Reinforcement Learning with Vector Quantized Encoding—0
Distributed Deep Reinforcement Learning: A Survey and A Multi-Player Multi-Agent Learning Toolbox—0
Correct-by-synthesis reinforcement learning with temporal logic constraints—0
Associative Memory Based Experience Replay for Deep Reinforcement Learning—0
A Generative Framework for Simultaneous Machine Translation—0
Distributed Deep Reinforcement Learning: An Overview—0
Distributed Deep Reinforcement Learning for Intelligent Load Scheduling in Residential Smart Grids—0
Deep RL-based Trajectory Planning for AoI Minimization in UAV-assisted IoT—0
A General Theory of Relativity in Reinforcement Learning—0
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Benchmark Results

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