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 12051–12100 of 15113 papers

TitleStatusHype
A Reinforcement Learning-Based Framework for Solving Physical Design Routing Problem in the Absence of Large Test Sets—0
How to Build User Simulators to Train RL-based Dialog SystemsCode0
Better Rewards Yield Better Summaries: Learning to Summarise Without ReferencesCode0
rlpyt: A Research Code Base for Deep Reinforcement Learning in PyTorchCode2
Generalization in Transfer Learning—0
Evolutionary reinforcement learning of dynamical large deviations—0
Classification Betters Regression in Query-based Multi-document Summarisation Techniques for Question Answering: Macquarie University at BioASQ7b—0
Logic and the 2-Simplicial Transformer—0
Reinforcement Learning-based Automatic Diagnosis of Acute Appendicitis in Abdominal CT—0
To Combine or Not To Combine? A Rainbow Deep Reinforcement Learning Agent for Dialog Policies—0
Scalable Reinforcement-Learning-Based Neural Architecture Search for Cancer Deep Learning Research—0
Generating Classical Chinese Poems from Vernacular ChineseCode0
Deep Reinforcement Learning with Distributional Semantic Rewards for Abstractive Summarization—0
Reinforcement learning with world model—0
PaccMann^RL: Designing anticancer drugs from transcriptomic data via reinforcement learning—0
Learning to Transfer Learn: Reinforcement Learning-Based Selection for Adaptive Transfer Learning—0
An Empirical Comparison on Imitation Learning and Reinforcement Learning for Paraphrase GenerationCode0
Guided Dialog Policy Learning: Reward Estimation for Multi-Domain Task-Oriented DialogCode0
Deep Actor-Critic Reinforcement Learning for Anomaly Detection—0
Reinforcement Learning: Prediction, Control and Value Function Approximation—0
Solving Math Word Problems with Double-Decoder Transformer—0
Ensemble-Based Deep Reinforcement Learning for Chatbots—0
Deep Reinforcement Learning for Chatbots Using Clustered Actions and Human-Likeness Rewards—0
Continuous Value Iteration (CVI) Reinforcement Learning and Imaginary Experience Replay (IER) for learning multi-goal, continuous action and state space controllersCode0
A Deep Reinforcement Learning Approach to Multi-component Job Scheduling in Edge Computing—0
OpenSpiel: A Framework for Reinforcement Learning in GamesCode3
Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning—0
Dynamics-aware EmbeddingsCode0
Universal Policies to Learn Them AllCode0
A Comparison of Action Spaces for Learning Manipulation Tasks—0
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision ProcessesCode0
Improving the dynamics of quantum sensors with reinforcement learning—0
Reinforcement Learning in Healthcare: A Survey—0
Practical Risk Measures in Reinforcement Learning—0
Opponent Aware Reinforcement LearningCode0
On Convergence Rate of Adaptive Multiscale Value Function Approximation For Reinforcement Learning—0
Analyzing Cyber-Physical Systems from the Perspective of Artificial Intelligence—0
A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy AdaptationCode0
Automated quantum programming via reinforcement learning for combinatorial optimizationCode0
Deep Reinforcement Learning for Foreign Exchange Trading—0
Dialog State Tracking with Reinforced Data Augmentation—0
A Deep Actor-Critic Reinforcement Learning Framework for Dynamic Multichannel Access—0
Learning to Sit: Synthesizing Human-Chair Interactions via Hierarchical Control—0
Reinforcement Learning is not a Causal problem—0
ARAML: A Stable Adversarial Training Framework for Text GenerationCode0
A Domain-Knowledge-Aided Deep Reinforcement Learning Approach for Flight Control Design—0
An Autonomous Performance Testing Framework using Self-Adaptive Fuzzy Reinforcement LearningCode0
A survey on intrinsic motivation in reinforcement learning—0
Transfer in Deep Reinforcement Learning using Knowledge Graphs—0
Mitigating Multi-Stage Cascading Failure by Reinforcement Learning—0
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Benchmark Results

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