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 14501–14550 of 15113 papers

TitleStatusHype
Stochastic Constraint Programming as Reinforcement Learning—0
Modular Multi-Objective Deep Reinforcement Learning with Decision ValuesCode0
Equivalence Between Policy Gradients and Soft Q-Learning—0
A Reinforcement Learning Approach to Weaning of Mechanical Ventilation in Intensive Care Units—0
Reinforcement Learning with External Knowledge and Two-Stage Q-functions for Predicting Popular Reddit Threads—0
Investigating Recurrence and Eligibility Traces in Deep Q-Networks—0
Beating Atari with Natural Language Guided Reinforcement LearningCode0
Effective Warm Start for the Online Actor-Critic Reinforcement Learning based mHealth Intervention—0
Pseudorehearsal in actor-critic agents—0
Task-Oriented Query Reformulation with Reinforcement LearningCode0
The Reactor: A fast and sample-efficient Actor-Critic agent for Reinforcement Learning—0
MUSE: Modularizing Unsupervised Sense EmbeddingsCode0
Ultrafast photonic reinforcement learning based on laser chaos—0
Optimizing Differentiable Relaxations of Coreference Evaluation MetricsCode0
Environment-Independent Task Specifications via GLTL—0
Virtual to Real Reinforcement Learning for Autonomous DrivingCode0
Deep Reinforcement Learning-based Image Captioning with Embedding Reward—0
Deep Q-learning from DemonstrationsCode0
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning—0
Composite Task-Completion Dialogue Policy Learning via Hierarchical Deep Reinforcement Learning—0
Data-efficient Deep Reinforcement Learning for Dexterous Manipulation—0
Stochastic Neural Networks for Hierarchical Reinforcement LearningCode0
Deep Reinforcement Learning framework for Autonomous DrivingCode0
Stein Variational Policy Gradient—0
Finite Sample Analyses for TD(0) with Function Approximation—0
Multi-Advisor Reinforcement Learning—0
On the Properties of the Softmax Function with Application in Game Theory and Reinforcement Learning—0
Evaluating Persuasion Strategies and Deep Reinforcement Learning methods for Negotiation Dialogue agents—0
Integrated Learning of Dialog Strategies and Semantic Parsing—0
Learning Visual Servoing with Deep Features and Fitted Q-IterationCode0
Sentence Simplification with Deep Reinforcement LearningCode0
Enter the Matrix: Safely Interruptible Autonomous Systems via Virtualization—0
Dynamic Computational Time for Visual AttentionCode0
Inverse Risk-Sensitive Reinforcement Learning—0
Inverse Reinforcement Learning from Summary Data—0
Socially Aware Motion Planning with Deep Reinforcement LearningCode0
Exploration--Exploitation in MDPs with Options—0
Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention—0
Unsupervised Basis Function Adaptation for Reinforcement Learning—0
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement LearningCode0
Fake News Mitigation via Point Process Based Intervention—0
Deep Exploration via Randomized Value Functions—0
Faster Reinforcement Learning Using Active SimulatorsCode0
Investigation of Language Understanding Impact for Reinforcement Learning Based Dialogue Systems—0
Black-Box Data-efficient Policy Search for RoboticsCode0
Pseudorehearsal in value function approximation—0
Multi-Timescale, Gradient Descent, Temporal Difference Learning with Linear Options—0
Online Learning for Offloading and Autoscaling in Energy Harvesting Mobile Edge Computing—0
Deep Decentralized Multi-task Multi-Agent Reinforcement Learning under Partial Observability—0
Minimax Regret Bounds for Reinforcement LearningCode0
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Benchmark Results

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