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 3721–3730 of 15113 papers

TitleStatusHype
Deep reinforcement learning for search, recommendation, and online advertising: a survey—0
Autonomous Unmanned Aerial Vehicle Navigation using Reinforcement Learning: A Systematic Review—0
A State Aggregation Approach for Solving Knapsack Problem with Deep Reinforcement Learning—0
Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management—0
Deep Reinforcement Learning for Simultaneous Sensing and Channel Access in Cognitive Networks—0
Deep Reinforcement Learning for Single-Shot Diagnosis and Adaptation in Damaged Robots—0
Accelerating Stochastic Composition Optimization—0
Autonomous Warehouse Robot using Deep Q-Learning—0
Corruption-Robust Offline Reinforcement Learning—0
Corruption-robust exploration in episodic reinforcement learning—0
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Benchmark Results

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