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 13151–13200 of 15113 papers

TitleStatusHype
MEETING BOT: Reinforcement Learning for Dialogue Based Meeting Scheduling—0
Quantum Adiabatic Algorithm Design using Reinforcement Learning—0
Generative Adversarial User Model for Reinforcement Learning Based Recommendation SystemCode0
Dealing with Limited Backhaul Capacity in Millimeter Wave Systems: A Deep Reinforcement Learning Approach—0
A New Concept of Deep Reinforcement Learning based Augmented General Sequence Tagging System—0
Learning to Walk via Deep Reinforcement Learning—0
Deconfounding Reinforcement Learning in Observational SettingsCode0
Optimizing Market Making using Multi-Agent Reinforcement Learning—0
VMAV-C: A Deep Attention-based Reinforcement Learning Algorithm for Model-based Control—0
Iroko: A Framework to Prototype Reinforcement Learning for Data Center Traffic ControlCode0
Escape Room: A Configurable Testbed for Hierarchical Reinforcement Learning—0
Learning to Navigate the Web—0
NADPEx: An on-policy temporally consistent exploration method for deep reinforcement learning—0
Pre-training with Non-expert Human Demonstration for Deep Reinforcement LearningCode0
Optimizing Quantum Error Correction Codes with Reinforcement Learning—0
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search—0
TD-Regularized Actor-Critic MethodsCode0
Universal Successor Features ApproximatorsCode0
Information-Directed Exploration for Deep Reinforcement LearningCode0
Incentive-based demand response for smart grid with reinforcement learning and deep neural network—0
Deep reinforcement learning for search, recommendation, and online advertising: a survey—0
Domain Adaptation for Reinforcement Learning on the Atari—0
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning AgentsCode0
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search—0
Fuzzy Controller of Reward of Reinforcement Learning For Handwritten Digit Recognition—0
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing—0
Malthusian Reinforcement Learning—0
Decentralized Computation Offloading for Multi-User Mobile Edge Computing: A Deep Reinforcement Learning ApproachCode0
Likelihood Quantile Networks for Coordinating Multi-Agent Reinforcement Learning—0
Residual Policy LearningCode0
Scaling shared model governance via model splitting—0
Guaranteed satisficing and finite regret: Analysis of a cognitive satisficing value function—0
IRLAS: Inverse Reinforcement Learning for Architecture SearchCode0
Learning to Communicate: A Machine Learning Framework for Heterogeneous Multi-Agent Robotic Systems—0
Exploration Conscious Reinforcement Learning RevisitedCode0
A predictive safety filter for learning-based control of constrained nonlinear dynamical systems—0
Efficient Model-Free Reinforcement Learning Using Gaussian Process—0
KF-LAX: Kronecker-factored curvature estimation for control variate optimization in reinforcement learning—0
Dialogue Generation: From Imitation Learning to Inverse Reinforcement LearningCode0
The Gap Between Model-Based and Model-Free Methods on the Linear Quadratic Regulator: An Asymptotic Viewpoint—0
Learning Montezuma's Revenge from a Single Demonstration—0
Communication-Efficient Policy Gradient Methods for Distributed Reinforcement Learning—0
Residual Reinforcement Learning for Robot Control—0
Measuring and Characterizing Generalization in Deep Reinforcement Learning—0
ToyBox: Better Atari Environments for Testing Reinforcement Learning AgentsCode0
Pseudo-Rehearsal: Achieving Deep Reinforcement Learning without Catastrophic ForgettingCode0
Active Deep Q-learning with Demonstration—0
Finite-Sample Analysis For Decentralized Batch Multi-Agent Reinforcement Learning With Networked Agents—0
Deep Reinforcement Learning and the Deadly Triad—0
Adapting Auxiliary Losses Using Gradient Similarity—0
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Benchmark Results

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