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 1276–1300 of 15113 papers

TitleStatusHype
Generalized Decision Transformer for Offline Hindsight Information MatchingCode1
On Effective Scheduling of Model-based Reinforcement LearningCode1
Intelligent Trading Systems: A Sentiment-Aware Reinforcement Learning ApproachCode1
Resilient Consensus-based Multi-agent Reinforcement Learning with Function ApproximationCode1
User Allocation in Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode1
Distilling Motion Planner Augmented Policies into Visual Control Policies for Robot ManipulationCode1
PowerGridworld: A Framework for Multi-Agent Reinforcement Learning in Power SystemsCode1
Reinforcement Learning for Mixed Autonomy IntersectionsCode1
"Good Robot! Now Watch This!": Repurposing Reinforcement Learning for Task-to-Task TransferCode1
A Dataset Perspective on Offline Reinforcement LearningCode1
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
B-Pref: Benchmarking Preference-Based Reinforcement LearningCode1
RLDS: an Ecosystem to Generate, Share and Use Datasets in Reinforcement LearningCode1
Curriculum Offline Imitation LearningCode1
Learning Large Neighborhood Search Policy for Integer ProgrammingCode1
Intrusion Prevention through Optimal StoppingCode1
On Joint Learning for Solving Placement and Routing in Chip DesignCode1
Attacking Video Recognition Models with Bullet-Screen CommentsCode1
URLB: Unsupervised Reinforcement Learning BenchmarkCode1
ABIDES-Gym: Gym Environments for Multi-Agent Discrete Event Simulation and Application to Financial MarketsCode1
DreamerPro: Reconstruction-Free Model-Based Reinforcement Learning with Prototypical RepresentationsCode1
Learning Domain Invariant Representations in Goal-conditioned Block MDPsCode1
Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution NetworksCode1
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Benchmark Results

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