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 13801–13850 of 15113 papers

TitleStatusHype
Cell Selection with Deep Reinforcement Learning in Sparse Mobile Crowdsensing—0
Lipschitz Continuity in Model-based Reinforcement LearningCode0
Dialogue Learning with Human Teaching and Feedback in End-to-End Trainable Task-Oriented Dialogue SystemsCode0
A Study on Overfitting in Deep Reinforcement LearningCode0
Automated vehicle's behavior decision making using deep reinforcement learning and high-fidelity simulation environment—0
Model-Free Linear Quadratic Control via Reduction to Expert Prediction—0
On Improving Deep Reinforcement Learning for POMDPs—0
State-Augmentation Transformations for Risk-Sensitive Reinforcement Learning—0
Learning How to Self-Learn: Enhancing Self-Training Using Neural Reinforcement Learning—0
CytonRL: an Efficient Reinforcement Learning Open-source Toolkit Implemented in C++Code0
Robust Dual View Deep Agent—0
Optimizing Query Evaluations using Reinforcement Learning for Web Search—0
Distort-and-Recover: Color Enhancement using Deep Reinforcement Learning—0
Feature-Based Aggregation and Deep Reinforcement Learning: A Survey and Some New Implementations—0
Emergence of Linguistic Communication from Referential Games with Symbolic and Pixel InputCode0
DORA The Explorer: Directed Outreaching Reinforcement Action-SelectionCode0
Universal Successor Representations for Transfer Reinforcement Learning—0
Market Making via Reinforcement LearningCode0
A clustering-based reinforcement learning approach for tailored personalization of e-Health interventions—0
Crafting a Toolchain for Image Restoration by Deep Reinforcement LearningCode0
Binary Space Partitioning as Intrinsic Reward—0
Outline Objects using Deep Reinforcement Learning—0
Gotta Learn Fast: A New Benchmark for Generalization in RLCode0
Latent Space Policies for Hierarchical Reinforcement Learning—0
Hierarchical Modular Reinforcement Learning Method and Knowledge Acquisition of State-Action Rule for Multi-target Problem—0
DeepMimic: Example-Guided Deep Reinforcement Learning of Physics-Based Character SkillsCode1
Scalable Sentiment for Sequence-to-sequence Chatbot Response with Performance Analysis—0
End-to-End Learning of Communications Systems Without a Channel ModelCode0
Programmatically Interpretable Reinforcement Learning—0
A Human Mixed Strategy Approach to Deep Reinforcement Learning—0
Information Maximizing Exploration with a Latent Dynamics Model—0
EmoRL: Continuous Acoustic Emotion Classification using Deep Reinforcement Learning—0
Renewal Monte Carlo: Renewal theory based reinforcement learning—0
StarCraft Micromanagement with Reinforcement Learning and Curriculum Transfer LearningCode0
Recall Traces: Backtracking Models for Efficient Reinforcement Learning—0
Learning to Run challenge solutions: Adapting reinforcement learning methods for neuromusculoskeletal environmentsCode0
Curiosity-driven Exploration for Mapless Navigation with Deep Reinforcement Learning—0
Learning to Run challenge: Synthesizing physiologically accurate motion using deep reinforcement learning—0
Snap Angle Prediction for 360^ Panoramas—0
Towards Learning Transferable Conversational Skills using Multi-dimensional Dialogue ModellingCode0
Learning to Navigate in Cities Without a MapCode0
Learning to Adapt in Dynamic, Real-World Environments Through Meta-Reinforcement LearningCode1
QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement LearningCode1
How an Electrical Engineer Became an Artificial Intelligence Researcher, a Multiphase Active Contours Analysis—0
Deep Reinforcement Learning for Traffic Light Control in Vehicular NetworksCode0
Unsupervised Predictive Memory in a Goal-Directed AgentCode0
Reinforcement learning for non-prehensile manipulation: Transfer from simulation to physical system—0
Reinforcement Learning for Fair Dynamic Pricing—0
Forward-Backward Reinforcement Learning—0
Learning Synergies between Pushing and Grasping with Self-supervised Deep Reinforcement LearningCode1
Show:102550
← PrevPage 277 of 303Next →

Benchmark Results

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