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 1281–1290 of 15113 papers

TitleStatusHype
User Allocation in Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode1
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power SystemsCode1
A Dataset Perspective on Offline Reinforcement LearningCode1
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task TransferCode1
Reinforcement Learning for Mixed Autonomy IntersectionsCode1
Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field ExperimentsCode1
Robust Deep Reinforcement Learning for Quadcopter ControlCode1
Cross Modality 3D Navigation Using Reinforcement Learning and Neural Style TransferCode1
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement LearningCode1
B-Pref: Benchmarking Preference-Based Reinforcement LearningCode1
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Benchmark Results

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