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 11626–11650 of 15113 papers

TitleStatusHype
Blind Inpainting of Large-scale Masks of Thin Structures with Adversarial and Reinforcement LearningCode0
Dynamic Pricing on E-commerce Platform with Deep Reinforcement Learning: A Field Experiment—0
Reinforcement Learning with Convolutional Reservoir Computing—0
Training Agents using Upside-Down Reinforcement LearningCode0
Scalable Reinforcement Learning for Multi-Agent Networked Systems—0
Reinforcement Learning Upside Down: Don't Predict Rewards -- Just Map Them to ActionsCode0
Reinforcement learning for bandwidth estimation and congestion control in real-time communications—0
Deep Model Compression Via Two-Stage Deep Reinforcement Learning—0
AlgaeDICE: Policy Gradient from Arbitrary Experience—0
Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning—0
Mo' States Mo' Problems: Emergency Stop Mechanisms from ObservationCode0
Optimal Policies Tend to Seek PowerCode0
Self-Learned Formula Synthesis in Set Theory—0
SafeLife 1.0: Exploring Side Effects in Complex EnvironmentsCode0
Policy Optimization Reinforcement Learning with Entropy Regularization—0
Human-Robot Collaboration via Deep Reinforcement Learning of Real-World Interactions—0
Just Ask:An Interactive Learning Framework for Vision and Language Navigation—0
A Model-Based Reinforcement Learning with Adversarial Training for Online RecommendationCode0
Learning Generalizable Device Placement Algorithms for Distributed Machine LearningCode0
Adaptive Auxiliary Task Weighting for Reinforcement LearningCode0
Learning Local Search Heuristics for Boolean SatisfiabilityCode0
Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement LearningCode0
Flow Rate Control in Smart District Heating Systems Using Deep Reinforcement Learning—0
Adversary A3C for Robust Reinforcement Learning—0
Learning Reward Machines for Partially Observable Reinforcement LearningCode0
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Benchmark Results

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