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 10126–10150 of 15113 papers

TitleStatusHype
Instance based Generalization in Reinforcement LearningCode0
Interpreting Graph Drawing with Multi-Agent Reinforcement Learning—0
Reinforcement Learning of Structured Control for Linear Systems with Unknown State Matrix—0
NEARL: Non-Explicit Action Reinforcement Learning for Robotic Control—0
Multi-Agent Reinforcement Learning for Visibility-based Persistent MonitoringCode0
Sample-efficient reinforcement learning using deep Gaussian processes—0
Shaping Rewards for Reinforcement Learning with Imperfect Demonstrations using Generative Models—0
Reinforcement Learning with Efficient Active Feature Acquisition—0
Production-based Cognitive Models as a Test Suite for Reinforcement Learning Algorithms—0
Reinforcement Learning with Imbalanced Dataset for Data-to-Text Medical Report Generation—0
Task-Completion Dialogue Policy Learning via Monte Carlo Tree Search with Dueling Network—0
Guided Dialogue Policy Learning without Adversarial Learning in the LoopCode0
Few-Shot Multi-Hop Relation Reasoning over Knowledge Bases—0
Topic-Preserving Synthetic News Generation: An Adversarial Deep Reinforcement Learning Approach—0
Personalized Multimorbidity Management for Patients with Type 2 Diabetes Using Reinforcement Learning of Electronic Health RecordsCode0
Abstract Value Iteration for Hierarchical Reinforcement Learning—0
Reinforcement Learning of Causal Variables Using Mediation Analysis—0
Machine versus Human Attention in Deep Reinforcement Learning Tasks—0
How do Offline Measures for Exploration in Reinforcement Learning behave?—0
Learning Personalized Discretionary Lane-Change Initiation for Fully Autonomous Driving Based on Reinforcement Learning—0
Learning to Represent Action Values as a Hypergraph on the Action Vertices—0
Learning to Unknot—0
Designing Interpretable Approximations to Deep Reinforcement Learning—0
DeepFoldit -- A Deep Reinforcement Learning Neural Network Folding Proteins—0
Reinforcement Learning for Sparse-Reward Object-Interaction Tasks in a First-person Simulated 3D Environment—0
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Benchmark Results

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