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 4381–4390 of 15113 papers

TitleStatusHype
Aesthetic Photo Collage with Deep Reinforcement Learning—0
A Fair Federated Learning Framework With Reinforcement Learning—0
A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning—0
A Family of Robust Stochastic Operators for Reinforcement Learning—0
A Federated Reinforcement Learning Framework for Link Activation in Multi-link Wi-Fi Networks—0
A Federated Reinforcement Learning Method with Quantization for Cooperative Edge Caching in Fog Radio Access Networks—0
A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation—0
Affordance as general value function: A computational model—0
Affordance-based Reinforcement Learning for Urban Driving—0
Affordance-Guided Reinforcement Learning via Visual Prompting—0
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Benchmark Results

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