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 2761–2770 of 15113 papers

TitleStatusHype
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection—0
Algorithms for Batch Hierarchical Reinforcement Learning—0
Achieving Tighter Finite-Time Rates for Heterogeneous Federated Stochastic Approximation under Markovian Sampling—0
Curriculum Learning Based on Reward Sparseness for Deep Reinforcement Learning of Task Completion Dialogue Management—0
Algorithmic Trading Using Continuous Action Space Deep Reinforcement Learning—0
Automated Driving with Evolution Capability: A Reinforcement Learning Method with Monotonic Performance Enhancement—0
Curriculum-based Deep Reinforcement Learning for Quantum Control—0
Algorithmic Prompt Generation for Diverse Human-like Teaming and Communication with Large Language Models—0
Automated Discovery of Functional Actual Causes in Complex Environments—0
Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning—0
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Benchmark Results

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