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 13001–13050 of 15113 papers

TitleStatusHype
Risk-Aware Active Inverse Reinforcement LearningCode0
Uncertainty-Based Out-of-Distribution Detection in Deep Reinforcement Learning—0
Credit Assignment Techniques in Stochastic Computation Graphs—0
A dual mode adaptive basal-bolus advisor based on reinforcement learning—0
A* Tree Search for Portfolio Management—0
Self-Learning Exploration and Mapping for Mobile Robots via Deep Reinforcement LearningCode0
What Should I Do Now? Marrying Reinforcement Learning and Symbolic Planning—0
Recurrent Control Nets for Deep Reinforcement Learning—0
Exploring applications of deep reinforcement learning for real-world autonomous driving systems—0
Deep Reinforcement Learning for Imbalanced ClassificationCode0
Hierarchical Reinforcement Learning via Advantage-Weighted Information MaximizationCode0
Accelerating Goal-Directed Reinforcement Learning by Model Characterization—0
Machine Teaching in Hierarchical Genetic Reinforcement Learning: Curriculum Design of Reward Functions for Swarm Shepherding—0
Optimal Decision-Making in Mixed-Agent Partially Observable Stochastic Environments via Reinforcement Learning—0
Imminent Collision Mitigation with Reinforcement Learning and Vision—0
A Computational Framework for Motor Skill Acquisition—0
Human-Like Autonomous Car-Following Model with Deep Reinforcement Learning—0
Complementary reinforcement learning towards explainable agents—0
Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds—0
Deep Reinforcement Learning for Multi-Agent Systems: A Review of Challenges, Solutions and Applications—0
Meta Reinforcement Learning with Distribution of Exploration Parameters Learned by Evolution Strategies—0
State representation learning with recurrent capsule networks—0
MEETING BOT: Reinforcement Learning for Dialogue Based Meeting Scheduling—0
Dealing with Limited Backhaul Capacity in Millimeter Wave Systems: A Deep Reinforcement Learning Approach—0
Generative Adversarial User Model for Reinforcement Learning Based Recommendation SystemCode0
Quantum Adiabatic Algorithm Design using Reinforcement Learning—0
Deconfounding Reinforcement Learning in Observational SettingsCode0
Learning to Walk via Deep Reinforcement Learning—0
A New Concept of Deep Reinforcement Learning based Augmented General Sequence Tagging System—0
Optimizing Market Making using Multi-Agent Reinforcement Learning—0
SNAS: Stochastic Neural Architecture SearchCode1
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
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search—0
Optimizing Quantum Error Correction Codes with Reinforcement Learning—0
TD-Regularized Actor-Critic MethodsCode0
Incentive-based demand response for smart grid with reinforcement learning and deep neural network—0
Universal Successor Features ApproximatorsCode0
Domain Adaptation for Reinforcement Learning on the Atari—0
Information-Directed Exploration for Deep Reinforcement LearningCode0
Deep reinforcement learning for search, recommendation, and online advertising: a survey—0
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
Malthusian Reinforcement Learning—0
Reinforcement Learning for Adaptive Caching with Dynamic Storage Pricing—0
An Atari Model Zoo for Analyzing, Visualizing, and Comparing Deep Reinforcement Learning AgentsCode0
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Benchmark Results

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