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 10751–10775 of 15113 papers

TitleStatusHype
Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles—0
Model-Based Meta-Reinforcement Learning for Flight with Suspended PayloadsCode1
Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning—0
Flexible and Efficient Long-Range Planning Through Curious Exploration—0
Chip Placement with Deep Reinforcement LearningCode1
AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning—0
Sequential Anomaly Detection using Inverse Reinforcement Learning—0
Tactical Decision-Making in Autonomous Driving by Reinforcement Learning with Uncertainty EstimationCode1
SIBRE: Self Improvement Based REwards for Adaptive Feedback in Reinforcement Learning—0
Reinforcement Learning to Optimize the Logistics Distribution Routes of Unmanned Aerial Vehicle—0
Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition—0
Never Stop Learning: The Effectiveness of Fine-Tuning in Robotic Reinforcement Learning—0
Self-Guided Evolution Strategies with Historical Estimated GradientsCode0
Tightening Exploration in Upper Confidence Reinforcement Learning—0
Learning as Reinforcement: Applying Principles of Neuroscience for More General Reinforcement Learning Agents—0
Data-Driven Learning and Load Ensemble Control—0
Energy-Based Imitation LearningCode1
Attention Routing: track-assignment detailed routing using attention-based reinforcement learning—0
Variational Policy Propagation for Multi-agent Reinforcement Learning—0
Superkernel Neural Architecture Search for Image Denoising—0
Modeling Survival in model-based Reinforcement Learning—0
Macro-Action-Based Deep Multi-Agent Reinforcement Learning—0
Time Adaptive Reinforcement Learning—0
F2A2: Flexible Fully-decentralized Approximate Actor-critic for Cooperative Multi-agent Reinforcement Learning—0
Deep Reinforcement Learning for Adaptive Learning Systems—0
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Benchmark Results

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