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

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Showing 13651–13700 of 15113 papers

TitleStatusHype
QuaRL: Quantization for Fast and Environmentally Sustainable Reinforcement LearningCode0
Quantile-Based Deep Reinforcement Learning using Two-Timescale Policy Gradient AlgorithmsCode0
QT-Opt: Scalable Deep Reinforcement Learning for Vision-Based Robotic ManipulationCode0
Swift Hydra: Self-Reinforcing Generative Framework for Anomaly Detection with Multiple Mamba ModelsCode0
SwiftRL: Towards Efficient Reinforcement Learning on Real Processing-In-Memory SystemsCode0
Swim: A General-Purpose, High-Performing, and Efficient Activation Function for Locomotion Control TasksCode0
Vision-based Navigation Using Deep Reinforcement LearningCode0
More Efficient Randomized Exploration for Reinforcement Learning via Approximate SamplingCode0
Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy CriticCode0
Q-Mixing Network for Multi-Agent Pathfinding in Partially Observable Grid EnvironmentsCode0
MORE-3S:Multimodal-based Offline Reinforcement Learning with Shared Semantic SpacesCode0
NerveNet: Learning Structured Policy with Graph Neural NetworksCode0
Switching to Discriminative Image Captioning by Relieving a Bottleneck of Reinforcement LearningCode0
Towards Diverse and Accurate Image Captions via Reinforcing Determinantal Point ProcessCode0
Towards Dynamic Trend Filtering through Trend Point Detection with Reinforcement LearningCode0
Neighborhood Mixup Experience Replay: Local Convex Interpolation for Improved Sample Efficiency in Continuous Control TasksCode0
Optimising Lockdown Policies for Epidemic Control using Reinforcement LearningCode0
Towards Effective Context for Meta-Reinforcement Learning: an Approach based on Contrastive LearningCode0
Constructing Parsimonious Analytic Models for Dynamic Systems via Symbolic RegressionCode0
Towards Effective Planning Strategies for Dynamic Opinion NetworksCode0
Optimality-based Analysis of XCSF Compaction in Discrete Reinforcement LearningCode0
Scaling All-Goals Updates in Reinforcement Learning Using Convolutional Neural NetworksCode0
Near-Optimal Representation Learning for Hierarchical Reinforcement LearningCode0
Optimal foraging strategies can be learnedCode0
QLBS: Q-Learner in the Black-Scholes(-Merton) WorldsCode0
QBSO-FS: A Reinforcement Learning Based Bee Swarm Optimization Metaheuristic for Feature SelectionCode0
Optimal Policies Tend to Seek PowerCode0
Symmetric Reinforcement Learning Loss for Robust Learning on Diverse Tasks and Model ScalesCode0
PyTupli: A Scalable Infrastructure for Collaborative Offline Reinforcement Learning ProjectsCode0
PyRep: Bringing V-REP to Deep Robot LearningCode0
Optimal Attack and Defense for Reinforcement LearningCode0
Umbrella Reinforcement Learning -- computationally efficient tool for hard non-linear problemsCode0
USPR: Learning a Unified Solver for Profiled RoutingCode0
UNAS: Differentiable Architecture Search Meets Reinforcement LearningCode0
Mid-flight Propeller Failure Detection and Control of Propeller-deficient Quadcopter using Reinforcement LearningCode0
MicroRacer: a didactic environment for Deep Reinforcement LearningCode0
Optimal Adaptive Prediction Intervals for Electricity Load Forecasting in Distribution Systems via Reinforcement LearningCode0
Towards Empathic Deep Q-LearningCode0
Towards End-to-End Learning for Dialog State Tracking and Management using Deep Reinforcement LearningCode0
Decomposition Methods with Deep Corrections for Reinforcement LearningCode0
Selective Token Generation for Few-shot Natural Language GenerationCode0
OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence GenerationCode0
Synthesis from Satisficing and Temporal GoalsCode0
Synthesising Reinforcement Learning Policies through Set-Valued Inductive Rule LearningCode0
Psychlab: A Psychology Laboratory for Deep Reinforcement Learning AgentsCode0
OptAGAN: Entropy-based finetuning on text VAE-GANCode0
Self-adaptive Torque Vectoring Controller Using Reinforcement LearningCode0
Towards End-to-End Reinforcement Learning of Dialogue Agents for Information AccessCode0
Synthesizing Neural Network Controllers with Probabilistic Model based Reinforcement LearningCode0
Towards Evaluating Adaptivity of Model-Based Reinforcement Learning MethodsCode0
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Benchmark Results

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