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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 13401–13450 of 15113 papers

TitleStatusHype
Curriculum goal masking for continuous deep reinforcement learning—0
Object-sensitive Deep Reinforcement Learning—0
Muscle Excitation Estimation in Biomechanical Simulation Using NAF Reinforcement LearningCode0
Transparency and Explanation in Deep Reinforcement Learning Neural Networks—0
Improvements on Hindsight Learning—0
Deterministic Implementations for Reproducibility in Deep Reinforcement LearningCode0
Adversarial Reinforcement Learning for Observer Design in Autonomous Systems under Cyber Attacks—0
Towards Better Interpretability in Deep Q-NetworksCode0
Visual Diagnostics for Deep Reinforcement Learning Policy Development—0
Model-Based Reinforcement Learning via Meta-Policy Optimization—0
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning ApproachCode0
Macquarie University at BioASQ 6b: Deep learning and deep reinforcement learning for query-based multi-document summarisation—0
Auto-tuning Distributed Stream Processing Systems using Reinforcement Learning—0
Improving Reinforcement Learning Based Image Captioning with Natural Language PriorCode0
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement LearningCode0
Deep Reinforcement Learning for Event-Triggered ControlCode0
Coordination-driven learning in multi-agent problem spaces—0
Image Captioning based on Deep Reinforcement Learning—0
Negative Update Intervals in Deep Multi-Agent Reinforcement LearningCode1
Multi-task Deep Reinforcement Learning with PopArtCode0
Reinforcement Learning in Topology-based Representation for Human Body Movement with Whole Arm Manipulation—0
Automatic, Personalized, and Flexible Playlist Generation using Reinforcement Learning—0
Combined Reinforcement Learning via Abstract RepresentationsCode0
SAI, a Sensible Artificial Intelligence that plays GoCode1
VPE: Variational Policy Embedding for Transfer Reinforcement Learning—0
Towards one-shot learning for rare-word translation with external experts—0
ViZDoom Competitions: Playing Doom from PixelsCode0
A Multi-Agent Reinforcement Learning Method for Impression Allocation in Online Display Advertising—0
Learning to Generate Structured Queries from Natural Language with Indirect Supervision—0
Combining imagination and heuristics to learn strategies that generalizeCode0
Adaptive Behavior Generation for Autonomous Driving using Deep Reinforcement Learning with Compact Semantic States—0
Improving Optimization Bounds using Machine Learning: Decision Diagrams meet Deep Reinforcement LearningCode0
Probabilistic Prediction of Interactive Driving Behavior via Hierarchical Inverse Reinforcement Learning—0
Learning Invariances for Policy GeneralizationCode0
ANS: Adaptive Network Scaling for Deep Rectifier Reinforcement Learning Models—0
Challenges of Context and Time in Reinforcement Learning: Introducing Space Fortress as a BenchmarkCode0
ARCHER: Aggressive Rewards to Counter bias in Hindsight Experience ReplayCode0
How to Combine Tree-Search Methods in Reinforcement Learning—0
Learn What Not to Learn: Action Elimination with Deep Reinforcement Learning—0
Model-Based Regularization for Deep Reinforcement Learning with Transcoder Networks—0
Accelerated Reinforcement Learning for Sentence Generation by Vocabulary PredictionCode0
Reinforcement Learning under ThreatsCode0
Recurrent World Models Facilitate Policy Evolution—0
Transferring Deep Reinforcement Learning with Adversarial Objective and Augmentation—0
Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning—0
Visual Transfer between Atari Games using Competitive Reinforcement LearningCode0
Natural Language Person Search Using Deep Reinforcement Learning—0
Effective Exploration for Deep Reinforcement Learning via Bootstrapped Q-Ensembles under Tsallis Entropy Regularization—0
Collaborative Deep Reinforcement Learning for Multi-Object Tracking—0
Goal-Oriented Visual Question Generation via Intermediate Rewards—0
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Benchmark Results

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