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 4001–4010 of 15113 papers

TitleStatusHype
An Analysis of Quantile Temporal-Difference Learning—0
Efficient Preference-Based Reinforcement Learning Using Learned Dynamics Models—0
Switchable Lightweight Anti-symmetric Processing (SLAP) with CNN Outspeeds Data Augmentation by Smaller Sample -- Application in Gomoku Reinforcement Learning—0
SoK: Adversarial Machine Learning Attacks and Defences in Multi-Agent Reinforcement Learning—0
Adversarial Online Multi-Task Reinforcement LearningCode0
Hint assisted reinforcement learning: an application in radio astronomyCode0
Learning to Perceive in Deep Model-Free Reinforcement LearningCode0
Actor-Director-Critic: A Novel Deep Reinforcement Learning Framework—0
Why People Skip Music? On Predicting Music Skips using Deep Reinforcement LearningCode0
Towards AI-controlled FES-restoration of arm movements: Controlling for progressive muscular fatigue with Gaussian state-space models—0
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Benchmark Results

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