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 9251–9300 of 15113 papers

TitleStatusHype
Reinforcement Learning Agents for Ubisoft's Roller Champions—0
A Deep Reinforcement Learning Approach for Ramp Metering Based on Traffic Video Data—0
Deep Reinforcement Learning for Long Term Hydropower Production Scheduling—0
Deep Reinforcement Learning for Stock Portfolio Optimization—0
Interactive Search Based on Deep Reinforcement Learning—0
Robust Domain Randomised Reinforcement Learning through Peer-to-Peer Distillation—0
Semi-Supervised Off Policy Reinforcement Learning—0
Transfer Learning for Efficient Iterative Safety Validation—0
MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences—0
Resolving Implicit Coordination in Multi-Agent Deep Reinforcement Learning with Deep Q-Networks & Game TheoryCode0
Emergence of Different Modes of Tool Use in a Reaching and Dragging Task—0
Combining Reinforcement Learning with Lin-Kernighan-Helsgaun Algorithm for the Traveling Salesman ProblemCode1
NavRep: Unsupervised Representations for Reinforcement Learning of Robot Navigation in Dynamic Human EnvironmentsCode1
The Architectural Implications of Distributed Reinforcement Learning on CPU-GPU Systems—0
Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement LearningCode1
Vehicular Cooperative Perception Through Action Branching and Federated Reinforcement Learning—0
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation—0
Reset-Free Lifelong Learning with Skill-Space PlanningCode1
Efficient Reservoir Management through Deep Reinforcement Learning—0
GAEA: Graph Augmentation for Equitable Access via Reinforcement LearningCode1
Battery Model Calibration with Deep Reinforcement Learning—0
Fever Basketball: A Complex, Flexible, and Asynchronized Sports Game Environment for Multi-agent Reinforcement Learning—0
Multi-agent navigation based on deep reinforcement learning and traditional pathfinding algorithm—0
RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal ControlCode1
Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation—0
ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging ResearchCode1
Demonstration-efficient Inverse Reinforcement Learning in Procedurally Generated Environments—0
Neural Dynamic Policies for End-to-End Sensorimotor Learning—0
Offline Meta-level Model-based Reinforcement Learning Approach for Cold-Start Recommendation—0
Model-Agnostic Learning to Meta-Learn—0
Partially Connected Automated Vehicle Cooperative Control Strategy with a Deep Reinforcement Learning Approach—0
DeepCrawl: Deep Reinforcement Learning for Turn-based Strategy Games—0
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment DesignCode0
Dynamic RAN Slicing for Service-Oriented Vehicular Networks via Constrained Learning—0
Designing a Prospective COVID-19 Therapeutic with Reinforcement Learning—0
Sample Complexity of Policy Gradient Finding Second-Order Stationary Points—0
Pareto Deterministic Policy Gradients and Its Application in 5G Massive MIMO Networks—0
A Safe Reinforcement Learning Architecture for Antenna Tilt Optimisation—0
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER—0
Driving-Policy Adaptive Safeguard for Autonomous Vehicles Using Reinforcement Learning—0
Coinbot: Intelligent Robotic Coin Bag Manipulation Using Deep Reinforcement Learning And Machine Teaching—0
Are Gradient-based Saliency Maps Useful in Deep Reinforcement Learning?—0
BSODA: A Bipartite Scalable Framework for Online Disease Diagnosis—0
Improving the Naturalness and Diversity of Referring Expression Generation models using Minimum Risk Training—0
Improving Neural Machine Translation for Sanskrit-English—0
Combining Cognitive Modeling and Reinforcement Learning for Clarification in Dialogue—0
Answer-driven Deep Question Generation based on Reinforcement Learning—0
A Learning-Exploring Method to Generate Diverse Paraphrases with Multi-Objective Deep Reinforcement Learning—0
ExpanRL: Hierarchical Reinforcement Learning for Course Concept Expansion in MOOCs—0
Text Simplification with Reinforcement Learning Using Supervised Rewards on Grammaticality, Meaning Preservation, and Simplicity—0
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Benchmark Results

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