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 1651–1700 of 15113 papers

TitleStatusHype
Offline Reinforcement Learning from Images with Latent Space ModelsCode1
Deep Reinforcement Learning for Joint Spectrum and Power Allocation in Cellular NetworksCode1
Generalize a Small Pre-trained Model to Arbitrarily Large TSP InstancesCode1
Multi-Decoder Attention Model with Embedding Glimpse for Solving Vehicle Routing ProblemsCode1
CityLearn: Standardizing Research in Multi-Agent Reinforcement Learning for Demand Response and Urban Energy ManagementCode1
Content Masked Loss: Human-Like Brush Stroke Planning in a Reinforcement Learning Painting AgentCode1
High-Throughput Synchronous Deep RLCode1
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement LearningCode1
Policy Gradient RL Algorithms as Directed Acyclic GraphsCode1
Reinforcement Learning for Contact-Rich Tasks: Robotic Peg Insertion StrategiesCode1
Sim-to-real reinforcement learning applied to end-to-end vehicle controlCode1
An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy SearchCode1
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
Models, Pixels, and Rewards: Evaluating Design Trade-offs in Visual Model-Based Reinforcement LearningCode1
GAEA: Graph Augmentation for Equitable Access via Reinforcement LearningCode1
Reset-Free Lifelong Learning with Skill-Space PlanningCode1
RLOC: Terrain-Aware Legged Locomotion using Reinforcement Learning and Optimal ControlCode1
ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging ResearchCode1
Learning Multi-Agent Communication through Structured Attentive ReasoningCode1
Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver?Code1
Revisiting Maximum Entropy Inverse Reinforcement Learning: New Perspectives and AlgorithmsCode1
Self-supervised Visual Reinforcement Learning with Object-centric RepresentationsCode1
Generalization in Reinforcement Learning by Soft Data AugmentationCode1
Interactive Machine Learning of Musical GestureCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
Optimization of the Model Predictive Control Update Interval Using Reinforcement LearningCode1
Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level PaintingsCode1
TLeague: A Framework for Competitive Self-Play based Distributed Multi-Agent Reinforcement LearningCode1
World Model as a Graph: Learning Latent Landmarks for PlanningCode1
Symmetry-Aware Actor-Critic for 3D Molecular DesignCode1
Evolutionary Planning in Latent SpaceCode1
An Empirical Study of Representation Learning for Reinforcement Learning in HealthcareCode1
Revisiting Rainbow: Promoting more Insightful and Inclusive Deep Reinforcement Learning ResearchCode1
Inverse Constrained Reinforcement LearningCode1
Adaptive Contention Window Design using Deep Q-learningCode1
Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?Code1
Combining Reinforcement Learning with Model Predictive Control for On-Ramp MergingCode1
Hierarchical clustering in particle physics through reinforcement learningCode1
Scalable Reinforcement Learning Policies for Multi-Agent ControlCode1
Learning Associative Inference Using Fast Weight MemoryCode1
NLPGym -- A toolkit for evaluating RL agents on Natural Language Processing TasksCode1
CDT: Cascading Decision Trees for Explainable Reinforcement LearningCode1
Tonic: A Deep Reinforcement Learning Library for Fast Prototyping and BenchmarkingCode1
PLAS: Latent Action Space for Offline Reinforcement LearningCode1
SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object ManipulationCode1
DeepMind Lab2DCode1
ROLL: Visual Self-Supervised Reinforcement Learning with Object ReasoningCode1
Optimizing Large-Scale Fleet Management on a Road Network using Multi-Agent Deep Reinforcement Learning with Graph Neural NetworkCode1
Gaussian RAM: Lightweight Image Classification via Stochastic Retina-Inspired Glimpse and Reinforcement LearningCode1
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Benchmark Results

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