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 6776–6800 of 15113 papers

TitleStatusHype
Towards Safe, Explainable, and Regulated Autonomous Driving—0
Triples-to-Text Generation with Reinforcement Learning Based Graph-augmented Neural Networks—0
HeterPS: Distributed Deep Learning With Reinforcement Learning Based Scheduling in Heterogeneous Environments—0
Explainable Biomedical Recommendations via Reinforcement Learning Reasoning on Knowledge Graphs—0
An Improved Reinforcement Learning Model Based on Sentiment Analysis—0
Learn Quasi-stationary Distributions of Finite State Markov Chain—0
Machine Learning for Mechanical Ventilation Control (Extended Abstract)—0
Uncertainty-aware Low-Rank Q-Matrix Estimation for Deep Reinforcement Learning—0
Reinforcement Learning with Adaptive Curriculum Dynamics Randomization for Fault-Tolerant Robot Control—0
Generalized Decision Transformer for Offline Hindsight Information MatchingCode1
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning—0
Lifelong Reinforcement Learning with Temporal Logic Formulas and Reward Machines—0
Reinforcement Learning on Human Decision Models for Uniquely Collaborative AI TeammatesCode0
Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement LearningCode0
Self-Learning Tuning for Post-Silicon Validation—0
SEIHAI: A Sample-efficient Hierarchical AI for the MineRL Competition—0
Post-processing Networks: A Method for Optimizing Pipeline Task-oriented Dialogue Systems using Reinforcement Learning—0
Empathetic Persuasion: Reinforcing Empathy and Persuasiveness in Dialogue Systems—0
Context-Aware Language Modeling for Goal-Oriented Dialogue Systems—0
A Multi-Document Coverage Reward for RELAXed Multi-Document Summarization—0
MAD for Robust Reinforcement Learning in Machine Translation—0
Probing the Robustness of Trained Metrics for Conversational Dialogue Systems—0
Deep Reinforcement Learning for Entity Alignment—0
Improving Learning from Demonstrations by Learning from Experience—0
Compressive Features in Offline Reinforcement Learning for Recommender Systems—0
Show:102550
← PrevPage 272 of 605Next →

Benchmark Results

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