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 7501–7550 of 15113 papers

TitleStatusHype
Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning—0
Sim-to-Lab-to-Real: Safe Reinforcement Learning with Shielding and Generalization Guarantees—0
Learning Multi-agent Skills for Tabular Reinforcement Learning using Factor Graphs—0
Safety-Aware Multi-Agent Apprenticeship Learning—0
Online POI Recommendation: Learning Dynamic Geo-Human Interactions in Streams—0
Anytime PSRO for Two-Player Zero-Sum Games—0
Look Closer: Bridging Egocentric and Third-Person Views with Transformers for Robotic Manipulation—0
Hybrid Reinforcement Learning-Based Eco-Driving Strategy for Connected and Automated Vehicles at Signalized Intersections—0
Accelerating Representation Learning with View-Consistent Dynamics in Data-Efficient Reinforcement Learning—0
K-nearest Multi-agent Deep Reinforcement Learning for Collaborative Tasks with a Variable Number of Agents—0
Differentially Private Reinforcement Learning with Linear Function Approximation—0
Conservative Distributional Reinforcement Learning with Safety Constraints—0
Programmatic Policy Extraction by Iterative Local Search—0
Toward Self-learning End-to-End Task-Oriented Dialog Systems—0
Towards deep observation: A systematic survey on artificial intelligence techniques to monitor fetus via Ultrasound Images—0
State of the Art of Reinforcement Learning—0
Spatiotemporal Costmap Inference for MPC via Deep Inverse Reinforcement Learning—0
Summarising and Comparing Agent Dynamics with Contrastive Spatiotemporal Abstraction—0
Railway Operation Rescheduling System via Dynamic Simulation and Reinforcement Learning—0
11 Summaries of Papers on Explainable Reinforcement Learning With Some Commentary—0
Designing realistic RL environment for power systems—0
An Understanding of Learning from Demonstrations for Neural Text Generation—0
An Improved Reinforcement Learning Algorithm for Learning to Branch—0
Implementations that Matter in Cooperative Multi-Agent Reinforcement Learning—0
Exploration by Random Network Distillation—0
Chaining Value Functions for Off-Policy Learning—0
Learning from Atypical Behavior: Temporary Interest Aware Recommendation Based on Reinforcement Learning—0
A Family of Cognitively Realistic Parsing Environments for Deep Reinforcement Learning—0
CONQRR: Conversational Query Rewriting for Retrieval with Reinforcement Learning—0
Inherently Explainable Reinforcement Learning in Natural Language—0
Long-Tail Classification for Distinctive Image Captioning: A Simple yet Effective Remedy for Side Effects of Reinforcement Learning—0
Jointly Reinforced User Simulator and Task-oriented Dialog System with Simplified Generative Architecture—0
Revisiting the Roles of “Text” in Text Games—0
Unsupervised Reinforcement Adaptation for Class-Imbalanced TextClassification—0
Reinforcement Learning with Large Action Spaces for Neural Machine Translation—0
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning—0
MUST: A Framework for Training Task-oriented Dialogue Systems with Multiple User SimulaTors—0
Profitable Strategy Design by Using Deep Reinforcement Learning for Trades on Cryptocurrency Markets—0
Recursive Least Squares Advantage Actor-Critic Algorithms—0
Deep Reinforcement Learning for Shared Autonomous Vehicles (SAV) Fleet Management—0
Block Policy Mirror Descent—0
Interpretable and Effective Reinforcement Learning for Attacking against Graph-based Rumor Detection—0
Comparing Model-free and Model-based Algorithms for Offline Reinforcement Learning—0
Smart Magnetic Microrobots Learn to Swim with Deep Reinforcement LearningCode0
Reinforcement Learning to Solve NP-hard Problems: an Application to the CVRP—0
Reinforcement Learning based Air Combat Maneuver Generation—0
Demystifying Reinforcement Learning in Time-Varying Systems—0
Weakly Supervised Scene Text Detection using Deep Reinforcement LearningCode0
Direct Mutation and Crossover in Genetic Algorithms Applied to Reinforcement Learning Tasks—0
Criticality-Based Varying Step-Number Algorithm for Reinforcement Learning—0
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Benchmark Results

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