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 4171–4180 of 15113 papers

TitleStatusHype
Adaptive Decision Making at the Intersection for Autonomous Vehicles Based on Skill Discovery—0
Adaptive Dialog Policy Learning with Hindsight and User Modeling—0
Adaptive Discounting of Training Time Attacks—0
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization—0
Policy Zooming: Adaptive Discretization-based Infinite-Horizon Average-Reward Reinforcement Learning—0
Adaptive Discretization in Online Reinforcement Learning—0
Adaptive Droplet Routing in Digital Microfluidic Biochips Using Deep Reinforcement Learning—0
Adaptive Energy Management for Real Driving Conditions via Transfer Reinforcement Learning—0
Adaptive Experience Selection for Policy Gradient—0
Adaptive Federated Learning and Digital Twin for Industrial Internet of Things—0
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Benchmark Results

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